1 Introduction

Many different control strategies have been proposed in the literature for robotic manipulation, mainly dealing with a specific problem and environment. Still, the proposed solutions often fail to achieve the necessary robustness and dexterity, especially with lightweight arm structures operating in open-ended environments and/or with inaccurate or soft bodies, noisy sensing and actuation, as in our case. Furthermore, many of the systems proposed in the literature are only simulated or very few tests in real-world environments have been performed. This is very limiting since the transition from simulation to reality always strongly reduces the system nominal performance (which are usually difficult to realistically evaluate in simulation). This appears very clearly in Bonsignorio and Zereik (2021), which explicitly demonstrated that, for lightweight, inaccurate robotic structures like H\(_2\)Arm, classic control algorithms are not robust enough whenever a substantial number of real-world tests are performed. In fact, the behaviour of a classic PID (Proportional Integral Derivative) control was there evaluated against a stochastic BSP (Belief Space Planning) methodology; conclusions drawn from such comparison highlighted a twofold important result: the BSP has a success rate remarkably higher than the PID (93.3 vs \(40.0\%\)), but in the meantime it is much slower (\(31.5\ s\) vs \(109.6\ s\) as mean execution time throughout all the experiments).

Recent approaches to the manipulation problem in unstructured and uncertain environments rely on machine learning techniques. Such methods typically exploit the reinforcement learning (RL) paradigm to derive controllers able to learn and adapt in order to successfully execute the required tasks. In most proposed approaches a deep neural network is employed, trained either on datasets featuring state variables or directly on images.

In our paper an imitation learning approach is proposed to train a low-accuracy low-cost arm to perform a simple reaching task, a critical and necessary step in every manipulation problem.

The adoption of the imitation learning strategy allows to cope with the effort required to perform experiments with a real robot. In fact, with respect to approaches based on reinforcement learning, exploiting the experience of an expert teacher avoids the need for a very large number of experiments before the controller starts learning something meaningful. Indeed, a very large number of experiments would be impossible in practice with a low-accuracy arm and uncertain setting such as those here considered. In particular, we implemented the behavioral cloning paradigm (Bain and Sammut 1999), which involves setting the problem of imitation as a supervised learning problem.

However, while in the typical imitation learning paradigm the controller learns from a human expert, we follow the less common approach of training from an algorithmically generated dataset, like for example in Seita et al. (2020) and train a deep neural network on datasets coming from a Belief Space Planning (BSP) algorithm, representing the teacher to be imitated by the network. The BSP belongs to the class of stochastic algorithms, which provide better performance with respect to deterministic ones in unstructured and dynamic environments. It is able to concurrently drive the manipulator to the goal and reduce uncertainty; hence, it is very suitable for manipulation in open-ended environments. The main drawback of the BSP approach is the computational burden to obtain the next action, which severely limits its real-time applicability. The introduction of a neural controller that learns to imitate its behavior is thus aimed at providing a controller that yields, in real-time, the same control action that the BSP would compute in the current conditions. In our case, the data obtained from the set of system trajectories corresponding to state-control action pairs computed through the BSP method are used to learn a mapping able to associate to each control the corresponding output.

In this context, the main objective of the work is not only to define an imitation learning technique for inaccurate structures, but also to support the analysis through real-world experimentation. To this purpose, a particularly difficult test architecture has been selected, consisting in a very inaccurate and non-repeatable structure, namely H\(_2\)Arm (Bonsignorio and Zereik 2021). Such system is an ad hoc designed, 3D-printed open-source tendon-driven robotic arm, with a total cost of less than 200 € overall and a weight of less than 1 kg. Its intrinsic unknown dynamics and uncertainties make it very suitable to simulate errors and non-modelled effects, in order to stress and test the algorithmic approach. H\(_2\)Arm motors are not endowed with any sensor (e.g. encoders) providing current position. The only feedback signal is produced by a simple low-quality camera mounted on the arm wrist. Hence, the robotic platform turns out to be very challenging for controllers, in terms of both uncertainties and unknowns: it is not possible to compute inverse and forward kinematics from measurements, and a model-free controller should be applied.

As in the previous work Bonsignorio and Zereik (2021), also the work reported here aims at the assessment and validation of the proposed control strategy through the conducted experimental real-world test campaign on H\(_2\)Arm.

In our experiments performed under various conditions the neural BSP-based controller yielded a strong improvement in the execution time with respect to the original BSP: overall, the neural controller turned out to be about 10 times faster than the BSP one, making it actually exploitable for real-time robust manipulation. Moreover, a great advantage of this system is its high experimental reproducibility: different controllers may be applied and their performance compared.

We remark that we tested our system in a laboratory context but we target the underwater environment for H\(_2\)Arm final application; this is one of the strongest motivations that justify the employment of a stochastic control framework integrated with a neural controller. In fact, the unstructured environment poses great challenges in terms of control performance and decisional autonomy, and is one of the major reasons why robots are not really employed in everyday tasks in close contact with humans, yet.

Concerning the proposed controller, although more advanced imitation learning strategies could be applied (such as, for instance, inverse reinforcement learning), behavioral cloning was chosen for our preliminary tests due to its simplicity. In fact, this basic imitation scheme proved to be good enough to yield suitable performance in our experiments. The developed deep learning controllers have been able to execute the required task successfully in all the tests; they showed good extrapolation properties and robustness also in case of noise, injected to stress the system and draw conclusion about its robustness.

The paper is organized as follows: in Sect. 2 a comprehensive literature overview is provided, while the experimental robotic platform is described in Sect. 3. The proposed control strategy is detailed in Sect. 4 and the obtained results are presented in Sect. 5. Finally, Sect. 6 draws conclusions and depicts the foreseen future research activities.

2 Related works

H\(_2\)Arm is a specific example of compliant robot arm. It is a common opinion that soft structure robots could, in principle, make the manipulation task easier, therefore, a number of partially compliant robots has been proposed. In particular tendon-driven manipulators have been proposed to imitate human arms driven by muscles. Traditional approaches to robotic manipulation control rely on linearized models and accurate sensing and actuation. Reducing weights and increasing compliance, as with H\(_2\)Arm and typically in soft robotics applications, leads to a dramatic increase in non-linearities and uncertainties both in the dynamics and the measures, thus making most widely used methods unsuitable. The most promising approaches are based on stochastic planning and control methods such as BSP, or through Machine Learning (ML) and in particular Deep Learning (DL). These approaches will be discussed later in this section. The GummiArm is an example of compliant robotic manipulator, similar to ours, 3D-printed and driven by a combination of accurate (and expensive) digital servos with encoders and viscoelastic composite tendons. It has been used to test several advanced bio-mimetic control strategies (Stoelen et al. 2016). A non-linear robust control strategy for a tendon-driven manipulator is presented in Okur et al. (2015). The proposed approach considers, like in our work, uncertainties and external disturbances. However, such control strategy needs to know actuator positions and velocities, and also forces generated on tendons; moreover, it has been tested only in simulation. A visual-servoing control for a cable-driven soft manipulator is reported in Wang et al. (2016), where the authors discuss the effect of system linearization and approximation, reducing system complexity but also sacrificing accuracy. A planning algorithm for a soft planar arm is proposed in Marchese et al. (2014), where a series of constrained optimization problems is solved to find a locally optimal inverse kinematics, introducing errors and limitations. The environment is supposed to be known a priori , which is a very strict and limiting hypothesis. In Subudhi and Morris (2009), soft computing techniques are applied to the control of a multi-link flexible manipulator. Such methodology is suited to control systems that are particularly difficult to be modelled, and provides a greater tolerance to imprecision. An overview of soft robotics approaches is presented in Rus and Tolley (2015), whose conclusion is that an effective control of such structures needs new models and algorithms to iteratively learn the necessary manipulation skills.

While, for example, advanced solutions leveraging on the group regularities in the movement and local displacements of mechanical systems (Bonsignorio 2013), have been proposed for the control of systems like ours, we have focused on a simpler and more widely used approach, i.e., BSP (Zereik et al. 2015; Agha-Mohammadi et al. 2014; Platt et al. 2010). Belief Space Planning belongs to the family of stochastic control approaches, aimed at increasing the effectiveness and robustness of control methods on ‘real-world robots’. In Bonsignorio and Zereik (2021) authors report on the performance comparison of a standard PID (Proportional Integral Derivative) and Belief Space Planning control implemented on H\(_2\)Arm. That work clearly demonstrates the capability of the BSP, with very rough tuning, to perform a simple reaching task. However, this method is too slow for real-time control. This motivated us, as told above, to find more effective solutions based on an imitation learning approach exploiting deep neural networks.

Machine Learning approaches have focused on the identification (and composition) of ‘atomic’ chunks of motion trajectories, ‘motion primitives’ and related planned tasks, ‘motor skills’ through Reinforcement Learning, for example based on policy gradients and various policy search techniques (more on this point later in the section). To cope with the real-time requirements of robots, other approaches have developed incremental online learning schemes suitable for high dimensional spaces. Similar methods have been applied to the learning of the inverse kinematics – the joint trajectories corresponding to the desired end-effector trajectories – of the robot itself. An adaptive neural network controller is proposed in Xie et al. (2010), but only simulation has been used to evaluate the approach, without any experimental validation. Other approaches that exploit neural networks are in Rolf et al. (2015), which is based on learning the inverse kinematics/statics of soft manipulators and Thuruthel et al. (2017), where the forward dynamics of the model is learned using a RNN (Recurrent Neural Network) in order to enable predictive control.

Reinforcement Learning and especially Deep Reinforcement Learning (DRL) methods have been adapted to various planning, grasping and manipulation problems. Efforts have been dedicated to make learning faster – implementing asynchronous and parallelization processes – and to develop realistic simulation – and appropriate simulation trial randomization procedures – to reduce the needed time-consuming learning runs on ‘real robots’, (Kober et al. 2013; Rusu et al. 2016). In Bonsignorio et al. (2020) some key theoretical issues are discussed, such as: how to exploit the manifold structure of data, how to characterise temporal features, how to implement unsupervised learning, and multifidelity reinforcement learning as well as the assurance and verification of autonomous operations, see Bonsignorio et al. (2020). Reference DL implementations of Reinforcement Learning approaches can be found in the DeepMind Control Suite (Tassa et al. 2018) and OpenAI Gym (OpenAI 2020). Both platforms implement a number of test cases in simulation in a partially reproducible way. New approaches based on Contrastive Unsupervised Learning have shown some promise, see Srinivas et al. (2020). Deep reinforcement learning is exploited in more recent research, as in Gu et al. (2017), which proposes the utilization of many different robots to gather data to train the neural network; this reduces the training time, but increases complexity (for instance, it is difficult to integrate different experiences learnt by different robots and to obtain a generalizable policy) and costs (many robots needed). A similar approach is adopted in Ebert et al. (2018), where a Model-Predictive Control (MPC) is combined with a retrying option to learn manipulation skills; as in previous work, a really huge amount of training data is collected and employed, using two different robots. A lower amount of training demonstrations is needed in Zhu et al. (2018), where a model-free deep reinforcement learning algorithm is applied to visuomotor tasks. However, a quite low success rate is obtained, when simulation results are transferred to real-world experiments.

The imitation learning framework, that we merge in our work with BSP for H\(_2\)Arm control, consists of a heterogeneous set of techniques able to emulate a control scheme through the imitation of the controlled system itself during its operating phase. The first techniques studied in this field are those referring to behavioral cloning (Bain and Sammut 1999). Some behavioral cloning methods make use of local models based on kernel functions and also provide notions of convergence to the reference controller (Macciò 2016; Macciò and Cervellera 2012); others use different approximators and are more application-oriented (Bojarski et al. 2016; Sammut et al. 1992). In order to overcome the issue of having a not enough representative dataset, some behavioral cloning methods have evolved further by introducing techniques to artificially increase the available dataset (Pomerleau 1989; Giusti et al. 2016). In particular, the method known as Dataset Aggregation (DAgger) (Ross 2011), which has recently shown promising performance in several applications (such as, e.g., Ross 2012), is noteworthy. These techniques, however, show limitations when applied in contexts where the specific target is complex and the control scheme to be imitated is implemented by one or more human operators. In this case, techniques that try to infer the latent reward that is at the base of the policy generated by the reference controller are preferred. Probably, the best known methods in this case are the Inverse Reinforcement Learning (Abbeel and Ng 2004) and its derivative, the Apprenticeship Learning (Ng and Russell 2000), which, as mentioned, propose to use the trajectories generated by the controlled system to infer the reward signal. This, in turn, allows to reconstruct the reference controller. Other methods belonging to this category are the (Deep) Maximum Entropy Inverse Reinforcement Learning (Ziebart et al. 2008; Wulfmeier et al. 2015), the Guided Cost Learning (Finn et al. 2016) and the Generative Adversarial Imitation Learning (Ho and Ermon 2016).

The imitation learning paradigm, for tasks similar to the one we study, has been proposed by several authors. In Rahmatizadeh et al. (2018) a cheaper manipulator is considered, being endowed with rigid links and better sensors w.r.t. H\(_2\)Arm. A behavioral cloning approach is applied to such platform in order to learn some manipulation tasks; again, a huge amount of data (acquired through demonstration by a human operator) is needed to train the system. A virtual reality-based teleoperation approach is employed for collection of (again, a lot of) training data in Zhang et al. (2018). In Odetti et al. (2020), a behavioral cloning approach based on reservoir computing, trained by inexperienced human teachers during a festival, was successfully implemented for the control of a marine Unmanned Surface Vessel (USV).

Almost all of the cited works exploiting machine learning techniques employ robots with quite complex and expensive structures (that can be easily modelled starting from the mechanics), with precise and accurate sensors and a reasonably repeatable behavior. We follow an approach similar to the one described in Seita et al. (2020). However, in comparison to that study, where the authors train the imitation learning systems on a synthetic dataset generated by a fabric simulator and an algorithmic supervisor that has access to complete state information, there are significant differences. In our case we use the data calculated by a stochastic planner.

3 H\(_2\)Arm

In this section we present H\(_2\)Arm, the reference soft robotic structure we consider for the proposed methodology. Both its hardware and software architectures are described, as well as its “standard” controller based on BSP.

3.1 Hardware and software architecture

H\(_2\)Arm, depicted in Fig. 1, is a 3D-printed low-cost and inaccurate manipulator designed and developed within the Joint Lab between the Institute of Marine Engineering of the Italian National Research Council and Heron Robots. It has 4 degrees of freedom (DOFs), \(J_1-J_4\); the first three are rotational joints, while the last one is a prismatic joint. Each rotational joint is equipped with two counteracting motors winding up a tendon-like rope. The last prismatic joint is directly moved by one motor, without the need of tendons. The joint configuration allows to perform visual-servoing tasks consisting in reaching by tracking and centering objects with respect to the image plane. H\(_2\)Arm is equipped with a commercial low-cost and inaccurate webcam, mounted on top the mobile part of \(J_4\), and hence moving integrally with the last prismatic joint. H\(_2\)Arm low-level is driven by a Pololu Mini Maestro 12-Channel USB Servo Controller (Pololu 2020), suitably generating PWM (Pulse Width Modulation) signals to drive the motors.

Fig. 1
figure 1

3D-printed H\(_2\)Arm and related reference frames: \(<c>\) is the camera frame centered on the webcam, and \(<w>\) is the world frame, assumed to be placed in the center of the first joint (the big yellow horizontal disk). The main defined kinematic quantities for H\(_2\)Arm control are also depicted (color figure online)

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The software architecture is composed by two different modules: i) the vision-based recognition algorithm, in charge of measuring the red blob 3D position, and written in C++; ii) the neural controller (that will be introduced in Sect. 4), in charge of driving H\(_2\)Arm to track and center the blob in the image plane, and written in Python. This last module receives the measurements about the red blob 3D position with respect to frame \(<c>\) (refer to Fig. 1) from the recognition algorithm through a standard TCP socket. The same simple vision-based recognition algorithm implemented and exploited in Bonsignorio and Zereik (2021) was employed to retrieve the red blob position in 3D space with respect to the arm camera. This allows to directly compare system performance with respect to different trials and controllers. H\(_2\)Arm introduces many uncertainties in the control: first of all, its tendon-based structure produces unwanted forces that affect motion; furthermore, the (very low-cost and inaccurate) motors do not provide reliable and repeatable motion, and the only available feedback comes from the camera, measuring the arm distance from the center of the object of interest. The camera itself introduces an error on the object estimation. A block diagram summarizing the H\(_2\)Arm architecture is depicted in Figure 2.

Fig. 2
figure 2

H\(_2\)Arm architecture

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3.2 Reference BSP controller

The reference control scheme under which H\(_2\)Arm operates is based on the Belief Space Planning algorithm. For details on theory and experiments of the BSP technique applied to H2Arm, the interested reader can refer to Bonsignorio and Zereik (2021) and related supplemental material (https://ieeexplore.ieee.org/ielx7/100/9535319/9186201/supp1-3014279.pdf?arnumber=9186201).

BSP is a stochastic control strategy able to concurrently: (i) optimize the goal-reaching probability, and (ii) reduce uncertainties acting on the system. The disturbance affecting the observations is represented by the algorithm with a linear Gaussian model and the assumption of maximum likelihood observations is made. As proved in Platt et al. (2010), this assumption allows to apply non-linear optimization techniques in the belief space (that is the juxtaposition of the mean and the covariance matrix columns of the state). The actual system state is, in fact, not directly measured and only its (Gaussian) probability density function (pdf) is assumed to be known (the pdf mean is close to the current arm position and the covariance matrix represents the confidence of this information).

In brief, the algorithm works as explained in the following:

  • a trajectory moving the end-effector from the current point to the next one is planned;

  • such trajectory is linearly approximated by Direct Transcription with a series of segmentsFootnote 1;

  • the algorithm moves piecewise on the trajectory. The transition between segment extremes is accomplished through LQR that computes the needed control action, namely the Cartesian velocity \(\eta ^*(t)\) to be followed by the end-effector. This procedure is iterated until the final goal is reached;

  • from the current reference point, the reference arm joint velocities \({\dot{q}}^*(t)\) are computed through inverse kinematics. Then, the reference motor velocities \(u^*(t)\) are estimated by the robot Low-Level Control (LLC), applying the motor model. The LLC is in charge of managing the physical interaction with the robot hardware;

  • at each step, the algorithm checks how much the computed plan contributes to the final goal reaching. If the planned trajectory deviates too much from the desired one, re–planning occurs.

The BSP algorithm is graphically summarized by the flowchart of Fig. 3.

Fig. 3
figure 3

Flowchart of the BSP algorithm and its relation with the neural controllers MDL and CDL. Note that \(m_0\) and \(m_{goal}\) are the initial and final belief state mean, respectively; \({\hat{m}}_{1:s}\) and \({\hat{u}}_{1:s}\) are the planned trajectories for the belief state mean and the related control actions; \(\eta ^*(t)\) is the Cartesian reference velocity for the end-effector, \({\dot{q}}^*(t)\) and \(u^*(t)\) are the reference joint trajectories and the motor velocity commands, respectively. Finally, \({\bar{q}}_j\) are the joint positions and \(\xi _1\), \(\xi _2\) are pre-defined thresholds related to the belief state and the Cartesian error, respectively. DT, LQR and EKF stand for Linear Quadratic Regulator, Direct Transcription and Extended Kalman Filter; IK and LLC denote Inverse Kinematics computation and Low-Level Control of the robot. The blocks whose behavior is incorporated in the CDL-BSP and MDL-BSP are highlighted in green and violet, respectively.

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Notice that the joint velocities \({\dot{q}}^*(t)\) are obtained exploiting a model of the robot equations. In our case, due to the inaccurate structure of H\(_2\)Arm, the resulting values are expected to be very noisy. Yet, the BSP algorithm is able to cope with these uncertainties. In fact, since the BSP control actions balance the objective to reach the goal with the one to reduce the covariance matrix of the observations, they can also be considered as information-gathering actions. Note that the planning is performed in a higher dimensional space (with respect to the state space), even in case of a coarse-grained approximation of the belief space. Furthermore, the belief state dynamics is non-linear, stochastic and inherently under-actuated (the number of control inputs for the physical system is lower than the belief space dimension). To this aim, it is possible to simplify this very complex problem through the already mentioned assumption of maximum likelihood observations. This assumption means that the current system state is supposed to be the most likely state according to the past observations and the performed actions; this is equal to state that the control actions are able to achieve their intended purpose, thus leading the system to the desired state.

4 DL-BSP control

Despite the robustness and efficiency of BSP, obtaining the optimal action for the current state requires a computational effort that is, in general, unfeasible in real-time tasks. Here, we trained a deep neural network to imitate (clone) the BSP controller behavior, and be able to replicate it in real-time without the aforementioned computational burden. We refer to this controller as DL-BSP.

We assume that all the notation in the following is referred to frame \(<c>\) (see Fig. 1). Let us assume the time horizon during which the task is performed is divided in T discrete intervals at which actions are performed and measurements are collected. Denote by \(r^* = [r^*_x,r^*_y,r^*_z]^\mathsf{T}\) the desired final position of the end-effector with respect to the target object. Define, for \(t=0,\dots ,T-1\), \(r(t) = [r_x(t),r_y(t),r_z(t)]^\mathsf{T}\) as the current end-effector position with respect to the object, estimated by the camera at time t. Finally, define the error \(e(t) = r(t) - r^*= [e_x(t),e_y(t),e_z(t)]^\mathsf{T}\) at time t. Figure 1 provides an overview of the defined kinematic vectors \(r^*\), \(r\left( t\right)\) and \(e\left( t\right)\) for H\(_2\)Arm in the execution of the required visual-servoing task.

In order to train the deep neural controller, the first step consists in defining the training set of pattern/targets for a supervised learning procedure. As said, the patterns are derived from the arm trajectories obtained by BSP, while the targets are derived from the corresponding actions.

To this purpose, recall that \(u^*(t)\) is the vector of the velocities assigned by BSP to the various motors at stage t. In particular, we write \(u^*(t) = [v_1^*(t),\dots ,v_M^*(t)]^\mathsf{T}\), where M is the total number of motors. At each stage t, the optimal Cartesian velocity reference \(\eta ^*(t)\) given the error e(t) is computed by the BSP algorithm, to which correspond the motor velocities \(u^*(t)\) obtained as described in Sect. 3.2.

We consider two setups, corresponding to two different training sets for the behavioral cloning procedure:

  • Cartesian Deep Learning (CDL) control: the deep learning model provides, at every temporal stage t, an approximation of the optimal Cartesian velocity \(\eta ^*(t)\); then, the corresponding motor velocities are computed through inverse kinematics and the LLC model.

  • Motor Deep Learning (MDL) control: the deep learning model directly estimates the vector of motor velocities \(u^*(t)\).

The Cartesian control involves the approximation of a generally smoother and lower-dimensional function with respect to the motor velocities. On the other hand, the motor control avoids the need to compute the pseudo-inverse of the Jacobian matrix that, in case of robots with many links and joints, can be very large and lead to numerical issues. Further, for some kinds of robots, the structure exact kinematic model could be unavailable.

Consider a set of N successful trials obtained with the BSP, i.e., tasks in which the robot successfully reached the target within a maximum number of steps. In the case of Cartesian control, for \(j = 1,\dots ,N\), define the j-th trajectory as

$$\begin{aligned} \tau _j = \{(e_j(0), \eta ^*_j(0)),\dots ,(e_j(T_j-1),\eta ^*_j(T_j-1))\} \end{aligned}$$

where the subscript j in e and \(e^*\) denotes the j-th trajectory and \(T_j\) is the final time before trajectory j converged to the target. Similarly, we define the j-th trajectory in the case of motor control as

$$\begin{aligned} \tau _j = \{(e_j(0), u^*_j(0)),\dots ,(e_j(T_j-1),u^*_j(T_j-1))\} \end{aligned}$$

According to the definitions above, a trajectory is the collection of the observed errors, paired with the corresponding feature of the BSP control scheme that we want to imitate with the learning model.

The aim of the imitation controller is to learn from these sets of trajectories a mapping from the observed errors and the desired BSP output feature, in order to estimate online the action that would be yielded by the BSP approach for the current state.

For the sake of introducing the imitation learning scheme, let us focus on the motor control setting and denote by u(t) the vector of motor velocities produced by the imitation controller at stage t. Notice that in the case of Cartesian control it is sufficient to replace u(t) with \(\eta ^*(t)\) in the rest of the section.

For the generation of the closed-loop control u(t), due to the dynamic nature of the robotic system, we consider as approximating architecture a neural network of the recurrent kind (RNN), i.e. a structure endowed with memory (see Goodfellow et al. 2016 for a detailed description of these models). The generic mathematical form is given by

$$\begin{aligned} u(t) = g(h(t),\theta _1) \end{aligned}$$
(1)

where \(\theta _1\) is a set of parameters to be optimized, g is the output function (e.g., a linear function), and h(t) is an internal state vector (usually called ”hidden state”) that keeps track of the evolution of the input, thus, implementing the memory mechanism. In particular, we have

$$\begin{aligned} h(t) = f(h(t-1),e(t),\theta _2) \end{aligned}$$
(2)

where f is the state equation ruling the internal state and \(\theta _2\) is the set of parameters devoted to the internal state evolution. In general, a RNN typically receives as input a regressor that contains the last l states, i.e., \(\delta _l(t) = [e(t),\dots ,e(t-l+1)]^\mathsf{T}\), and applies recursively the equations (1) and (2) l times determining the output u(t). Figure 4 provides a graphical representation of a RNN as described by equations  (1) and (2).

Fig. 4
figure 4

Graphical representation of the RNN structure implementing the proposed controller

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The optimization of the sets of parameters \(\theta _1\) and \(\theta _2\) corresponds to the training procedure of the approximating architecture. This operation relies on the minimization of an empirical cost defined on the basis of the observed (error/BSP control) pairs from the successful trajectories. More specifically, we define the following mean squared error

$$\begin{aligned} J(\theta _1,\theta _2) = \sum _{j=1}^N \sum _{t=0}^{T_j - 1} \Vert {u_j(t)-u_j^*(t)}\Vert ^2, \end{aligned}$$

where \(u_j(t)\) is the output of the RNN in correspondence to the regressor \(\delta _l^{(j)}(t) = [e_j(t),\dots ,e_j(t-l+1)]^\mathsf{T}\). The optimal parameters \(\theta _1^*\) and \(\theta _2^*\) are determined by minimizing the cost J; this can be done using standard training procedures relying on the well-known backpropagation algorithm applied to the RNN structure (Goodfellow et al. 2016).

For the imitation of the BSP scheme we consider deep RNNs, i.e., neural architectures obtained by a cascade of recurrent layers. However, we employ a more sophisticated version of the “naive” RNN presented in equations (1) and (2); it is in fact known that such neural networks suffer from some structural problems, such as the vanishing/exploding gradient (Goodfellow et al. 2016). Therefore, in order to implement the recurrent deep architecture, we make use of multiple layers of the popular Gated Recurrent Unit (GRU) (Cho et al. 2014) with a linear output layer, which is more robust in the training phase with respect to simple RNNs.

The parts of the standard BSP whose behavior is imitated by the neural controllers (CDL and MDL) are highlighted in the flowchart of Fig. 3.

5 Experiments

The imitation learning strategies described above were implemented and tested on H\(_2\)Arm. The tests involved the execution of a visual-servoing task consisting in centering a red blob in the image plane, acquiring 3D information on the error through a camera. This task was chosen to be the same as in Bonsignorio and Zereik (2021), in such a way to have a basis for comparison. For details about H\(_2\)Arm hardware and the software implementation of the task, refer to Sect. 3.1.

Our tests were designed to evaluate the performance of the deep neural controller (introduced in Sect. 4) when used in place of the BSP-based one. Both kinds of neural control methods described in Sect. 4 (i.e. Cartesian and motor) were considered. The tests also involved additional noise injected in the system in such a way to evaluate system robustness. In the remainder of the section we provide a detailed description of the setup for the testing campaign, and present the main results of the experiments , together with some discussion.

5.1 Experimental setup

The neural controllers were trained using data collected from \(N = 97\) successful trajectories generated by the BSP approach. The training trajectories always started with the red blob in view (totally or partially) in two of the four corners of the image, namely the top right corner - denoted as ‘Ref1’ - and the bottom left corner - denoted as ‘Ref2’ (the top left and bottom right corners are denoted as ‘Ref3’ and ‘Ref4’, respectively). The relative position between the red blob and the arm wrist, estimated by the robot camera, was used (as the only available information about the arm end-effector) to train the neural controllers, together with the corresponding commands sent to motors. As said, a noise was injected into the system, more specifically added to the end-effector position r(t) estimated by the camera at time t. In particular, since our long-term objective is underwater manipulation, the generated noise is distributed according to the Pierson-Moscowitz spectrum (Pierson and Moskowitz 1964). According to this formulation, the sea elevation is expressed as

$$\begin{aligned} \zeta = \sum _{j=1}^{W} A_j\sin {\left( \omega _jt-k_jx+\epsilon _j\right) } \end{aligned}$$
(3)

where for each wave \(j = 1,\dots ,W\), \(A_j\) is the amplitude, \(\omega _j\) is the angular frequency, \(k_j\) is the wave number and \(\epsilon _j\) is a random phase angle; x is the wave location from the origin. Angles \(\epsilon _j\) are constant and uniformly distributed in the range \(\left[ 0, 2\pi \right]\); for deep water, the relation \(k_j=\frac{\omega _j^2}{g}\) holds. In these experiments, the additional noise generated at each step accordingly to the Pierson-Moskowitz spectrum was of the form \(n=w \zeta\) along each considered axis, where w is a scaling factor. We tested our system versus two different noise magnitudes, namely \(w=0.035\) and \(w=0.05\); an example of the temporal behavior of such noise for each scale factor is shown in Fig. 5.

Fig. 5
figure 5

Samples of generated noise for \(w=0.035\) and \(w=0.05\)

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The scale factor values were chosen in light of the standard BSP experimental campaigns previously conducted and detailed in Bonsignorio and Zereik (2021). The BSP training trajectories have been executed both with or without additional noise injected into the system. In particular, 34 trajectories were executed without noise, and the remaining 63 were executed with added noise as described above. Note that in the training trajectories different scale factors w were tested (for details please consult Bonsignorio and Zereik 2021). Figures 6 and 7 illustrate some examples of BSP training trajectories with and without injected noise.

Fig. 6
figure 6

Examples of BSP training trajectories (without injected noise)

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Fig. 7
figure 7

Examples of BSP training trajectories (with injected noise)

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Concerning the deep models training process, the parameter l, as explained in Sect. 4, was chosen equal to 3, thus the N trajectories yielded a total number \(N^* = 1801\) of input/output pairs used to train the neural models.

The deep neural models employ GRU neural units, as said in Sect. 4, for the recurrent layers. In particular, for the Cartesian control we used a network with 2 layers of 50 GRU units, while for the motor control, due to the more complex dynamics and larger output, we employed 5 layers of 500 GRU units. The network parameters have been optimized with the ADAM algorithm (Goodfellow et al. 2016), using batches made of 16 samples. All the network models have been implemented with PyTorchFootnote 2.

For each neural controller, 20 tests were performed without noise injection and 20 with noise injection with \(w=0.035\). Each set of 20 experiments was equally distributed among the 4 different initial conditions (i.e., red blob initially in view in all the 4 corners of the image). Hence, a total number of 80 test experiments was performed. The test starting points in ‘Ref1’ and ‘Ref2’ are always different with respect to those of the training trajectories. In general, “starting from Refn” actually means “starting from a random point in the corner area Refn”. Concerning the tests starting from ‘Ref3’ and ‘Ref4’ (i.e., the corners never used during the generation from the training trajectories), they have been included to evaluate the ability of the neural controller to learn and extrapolate, not only to imitate. After these tests, we conducted some in-depth analysis on the MDL controller, focusing on: i) the effect of varying the value of the neural controller parameter l; ii) the effect of a larger noise (\(w=0.05\)) acting on the system.

To evaluate the performance of the neural controllers, we employ the norm of the Cartesian error (expressed in \(\left[ m\right]\)) between H\(_2\)Arm end-effector current position and its desired final position (right in front of the red blob). This is used to compute two main performance indices:

  • the number of motion steps required to reach the goal (here represented by the error threshold level \(e^* = 0.03\) m), starting from an initial common distance (here set to 0.18 m);

  • the actual execution time (in seconds) required to reach the goal as above.

5.2 Results

Both the Cartesian and motor controls allowed to obtain \(100\%\) success rate in all tests, with and without injected noise. Table 1 reports a summary at a glance of the obtained results. In particular, it reports the average number of steps and the average time needed to reach the goal over the above-mentioned sets of 20 test trajectories for both the Cartesian and the motor deep-learning controllers, both with and without noise. The mean time-per-step of the single trajectory, averaged over the set of test trajectories, is also reported. For a comparison, the average number of steps, the average execution time and the mean time-per-step are reported in the table also for the 97 trajectories used for the training, obtained with the BSP algorithm.

Table 1 Comparison of the mean number of steps, the mean execution time and the mean time-per-step between the training and test trajectories for both neural controllers, both with and without additional noise. Note that the training trajectories correspond to the 97 successful BSP experiments described in Bonsignorio and Zereik (2021)
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Overall, the table shows how the proposed methodology proved to be quite effective in providing a controller able to successfully execute the task. In fact, we can see that, even if it takes a few more steps, the neural controller always yields convergence almost 10 times faster than the original BSP algorithm in terms of execution time.

Fig. 8
figure 8

Norm of the error and corresponding component-wise behavior in two example trajectories - Cartesian control without injected noise. Notice that trajectory 5 starts from Ref1, while trajectory 11 starts from Ref3

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Fig. 9
figure 9

Norm of the error and corresponding component-wise behavior in two example trajectories - Cartesian control with injected noise. Notice that trajectory 4 starts from Ref1, while trajectory 19 starts from Ref4

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Concerning the Cartesian control, Figs. 8 and 9 report the norm and corresponding component-wise behavior of the error in two example trajectories, without and with injected noise, respectively.

Fig. 10
figure 10

Mean over the 20 test trajectories of the number of steps required to reach decreasing error levels, without (left) and with (right) injected noise - Cartesian control

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Figure 10 illustrates the mean (over the 20 test trajectories) number of steps required to reach decreasing error levels, both for the noise-free and the noisy tests. These graphs show how relaxing the final error threshold requirement (e.g., setting it to \(0.05\ m\)) the system would reach the goal in a considerably smaller number of steps.

Regarding the motor control, Figures 11 and 12 report the norm and corresponding component-wise behavior of the error in two example trajectories, without and with injected noise, respectively.Footnote 3

Fig. 11
figure 11

Norm of the error and corresponding component-wise behavior in two example trajectories - motor control without injected noise. Notice that trajectory 9 starts from Ref1, while trajectory 16 starts from Ref4

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Fig. 12
figure 12

Norm of the error and corresponding component-wise behavior in two example trajectories - motor control with injected noise. Notice that trajectory 1 starts from Ref2, while trajectory 19 starts from Ref4

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Fig. 13
figure 13

Mean over the 20 test trajectories of the number of steps required to reach decreasing error levels, without (left) and with (right) injected noise - motor control

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Figure 13 illustrates also for this control scheme the mean (over the 20 test trajectories) number of steps required to reach decreasing error levels, both for the noise-free and the noisy tests.

5.3 Additional experiments

After the first test campaign, a series of ad-hoc additional tests have been performed to investigate the robustness of the controller. In particular, we investigated how changing the value of parameter l in the neural controller affects the system behavior, as well as testing the performance of the controller in presence of larger noise. For these tests we focused on the MDL scheme, which is the most interesting one. In fact, it does not require the pseudo-inversion of the robot Jacobian matrix that, in case of more complex robots, can generally be of large size or, in case of soft and compliant robots, could be highly inaccurate. In the first round of additional experiments we performed the same tests as in the previous experimental campaign using \(l = 2\) and \(l = 5\). For each parameter value, 40 experimental runs were performed, 20 with and 20 without additional noise, and with the blob starting position equally distributed among the 4 image corners. Performance of the system when using \(l = 2\) and \(l = 5\) are reported in Fig. 14 (left); the system behavior is quite similar in all the cases, with a slight improvement in the case \(l=3\), showing that the parameter l is not a crucial factor for the success of the method, which is \(100\%\) in all cases.

Then we conducted a last case study aimed at assessing the MDL system behavior while facing additional noise with a larger magnitude. Thus, 20 more runs (5 with the red blob initial position in each image corner) were performed injecting in the system the already described Pierson-Moscowitz distributed spectrum with \(w = 0.05\). Success rate is again \(100\%\); the system behavior is depicted in Fig. 14 (right). From the plots, it is clear how the system behavior remains quite the same when facing a larger noise (the number of required steps is slightly higher but the difference is very small), thus underlining the system robustness to environmental disturbance.

Fig. 14
figure 14

System behaviour while facing variation in: neural controller parameter l (left) and additional noise magnitude (right)

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5.4 Discussion

All the performed tests were successful and had quite good performance in terms of convergence and execution time. Regarding the CDL-BSP, all 20 test trajectories without noise are such that the goal is reached in few steps; only in some cases it shows some oscillations. These oscillations are mainly due to the camera estimation error and, in any case, they do not prevent task completion, even if the threshold below which the task is considered completed is quite low (\(0.03\ m\)). Not surprisingly, the most oscillating component is the one along axis z of frame \(<c>\), namely the distance of the end-effector from the red blob along the camera optical axis. The trajectories with noise exhibit more sever oscillations, and such behavior is again mostly due to the noise and the estimation error on axis z of camera frame \(<c>\). The MDL-BSP scheme turns out to yield similar performance, with respect to the Cartesian one, in the noise-free case; only it takes on average some more steps and some more time (the mean time difference is \(0.55\ s\)). Instead, in the noisy case the motor deep-learning controller employs, on average, less steps and less time with respect to the CDL-BSP (the mean time difference is \(2.33\ s\)).

This difference is probably due to the fact that the CDL controller relies on a classical inverse kinematics approach to achieve the transition between the Cartesian and the joint operative spaces, while the MDL controller directly generates reference velocity for motors, without the need of an inverse kinematics computation. The first case likely implies a higher sensitivity to noise, since this last is filtered by the pseudo-inverse of the Jacobian matrix, while in the MDL case the reference commands are directly generated and sent to motors.

As already hinted, we computed the mean time per each step. As expected, this value is constant for both neural controllers, both with and without noise. On the contrary, it varies in the case of the standard BSP approach. This is due to the difference in terms of execution time between the neural and standard control strategies: the time employed by neural controllers to compute the next command to be sent to the robot is negligible with respect to the motion execution time, where as the BSP computation time (needed to solve the optimization problem at each sample time) is in general greater than the motion execution time (and potentially even higher in case of replanning).

For the sake of providing an example of comparable task, Figure 15 illustrates the DeepMind “Reacher” task (Tassa et al. 2018) along with our visual-servoing one, highlighting the main differences.

Fig. 15
figure 15

Comparison among the proposed H\(_2\)Arm visual-servoing task and the “Reacher” task of the DeepMind Control Suite. The main characteristics of each task are: a) DeepMind Reacher – a 2-link planar structure that has to reach a target, executed only in simulation, with known proprioceptive measures, known target location, simulated scenario with known noise structure, many training data needed, AI directly on image pixels. Tasks are strongly observable, position and velocity observations depend only on the current state. Sensor readings only depend on the previous transition, see Tassa et al. (2018). Courtesy of DeepMind. b) H\(_2\)Arm – 4-link 3D manipulator, experimented in real world, without proprioceptive information (the only sensor on-board is the wrist-mounted camera), additional noise injected in some of the experiments, very few needed training data (97 BSP trajectories logged in previous tests where the arm was controlled by the BSP algorithm only, without any neural controller), images are pre-processed by a vision algorithm and AI works on measures estimated by vision

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6 Conclusions and future work

The proposed deep neural controllers turned out to be quite effective for the considered visual-servoing blob centering task. DL-BSP allowed to obtain \(100\%\) success rate in all our tests both with Cartesian and motor control schemes, proving to be robust towards both camera estimation errors (above all on depth z component wrt frame \(<c>\)) and additional Pierson-Moskowitz noise directly injected on the red blob 3D position measurement at each sample time. This confirms that the combination of H\(_2\)Arm structure and the proposed control schemes is a good candidate for the control of light inaccurate arms. Both controllers exhibited the capability to extrapolate the tasks learned by imitation. In fact, in all the trajectories starting in the Ref3 and Ref4 corners, the arm end-effector always reached the desired final position in front of the red blob, even if no trajectory starting in those corners was provided in the training set. Looking at Table 1, we can notice that the controller trained on the Cartesian dataset is faster than the one trained on the motor velocity dataset in tests without noise, while the latter is faster in tests with noise; the same alternate behavior can be reported analysing the required steps for both controllers (the Cartesian control scheme needs less steps than the motor one for the case without additional noise, while it takes more steps when additional noise is injected into the system). The performance of both controllers slightly deteriorates when additional noise is injected into the system, but the times remain remarkably smaller than the standard BSP control scheme behavior.

The motor control strategy, by directly generating commands to the arm motors, can result more efficient in terms of online computational burden, since it does not require the pseudo-inversion of the robot Jacobian matrix, as already hinted. Overall, in our experiments the deep learning imitation approach allowed to retain the optimality and strong robustness properties of the BSP algorithm, while allowing fast execution times that make it actually applicable to real-time operations. Thus, it appears to be a quite promising option to enable the successful operation of low-cost, modular, inaccurate and non-repeatable robotic structures such as H\(_2\)Arm.

Moreover, according to preliminary experiments that we additionally performed by manually displacing the red blob in the scene, we expect that the same approach will work for moving targets too, only relying on the same dataset and without further training. In fact, the system showed good performance also in such preliminary tests, whose results are comparable to the previously conducted experiments.

Future work will be aimed at performing more complex tasks, investigate the robustness to different training dataset, testing in unstructured environments (and marine contexts in particular) also comparing against more advanced imitation learning schemes (e.g., inverse reinforcement learning). From an experimental point of view, the middle-to-long term objective of the present research activity will focus on testing the proposed control strategy on a marinized manipulator (possibly with a more accurate structure allowing for ground-truth) in a relevant underwater environment, to compare system performance.

An interesting issue is to get a better understanding of the reduced training effort of the imitation deep learning network when the training sets are obtained through an application of an optimized algorithm such as the belief space planning (whether we consider the Cartesian or motor velocities approach). We have implemented our learning and control strategy on an open-source real-world arm performing a task similar to that of the ‘reacher’ scenario in Tassa et al. (2018) with a more complex robot on a very low-cost low-accuracy platform with no proprioceptive sensors and very basically actuated motors. We will investigate if and how our approach can be scaled to more complex tasks, in order to determine the real reach of the proposed methodology.