Sim-to-Real Reinforcement Learning for Deformable Object Manipulation (1806.07851v2)

Abstract: We have seen much recent progress in rigid object manipulation, but interaction with deformable objects has notably lagged behind. Due to the large configuration space of deformable objects, solutions using traditional modelling approaches require significant engineering work. Perhaps then, bypassing the need for explicit modelling and instead learning the control in an end-to-end manner serves as a better approach? Despite the growing interest in the use of end-to-end robot learning approaches, only a small amount of work has focused on their applicability to deformable object manipulation. Moreover, due to the large amount of data needed to learn these end-to-end solutions, an emerging trend is to learn control policies in simulation and then transfer them over to the real world. To-date, no work has explored whether it is possible to learn and transfer deformable object policies. We believe that if sim-to-real methods are to be employed further, then it should be possible to learn to interact with a wide variety of objects, and not only rigid objects. In this work, we use a combination of state-of-the-art deep reinforcement learning algorithms to solve the problem of manipulating deformable objects (specifically cloth). We evaluate our approach on three tasks --- folding a towel up to a mark, folding a face towel diagonally, and draping a piece of cloth over a hanger. Our agents are fully trained in simulation with domain randomisation, and then successfully deployed in the real world without having seen any real deformable objects.

• The paper extends deep reinforcement learning to deformable object manipulation using sim-to-real transfer with domain randomization.
• It employs an enhanced DDPG algorithm to train policies for cloth tasks, reaching simulation success rates from 77% to 90%.
• The study shows that end-to-end DRL enables effective robotic handling of deformable objects in dynamic, unstructured real-world environments.

Sim-to-Real Reinforcement Learning for Deformable Object Manipulation

The paper "Sim-to-Real Reinforcement Learning for Deformable Object Manipulation" addresses a challenging problem within robotics: the manipulation of deformable objects. Unlike rigid objects, deformable ones present a large configuration space due to their fluid behavior, making traditional modeling approaches often inefficient and inadequate. This research proposes utilizing end-to-end learning via reinforcement learning (RL) to develop control policies that are directly transferable from simulation to real-world applications without explicit modeling of object deformation.

Methodology

The paper harnesses Deep Reinforcement Learning (DRL), specifically leveraging an enhanced Deep Deterministic Policy Gradients (DDPG) algorithm to tackle three tasks involving cloth manipulation: folding a towel to a specific mark, diagonal folding of a small towel, and draping a towel over a hanger. The agents are trained entirely in a simulated environment with domain randomization techniques ensuring variability in environmental parameters like texture and lighting. Key to the approach is the use of domain randomization to achieve simulation-to-reality (sim-to-real) transfer without any further training on real hardware.

Results

The numerical evaluation of this approach indicates that the trained agents successfully execute the designated tasks in both simulated and real-world environments. The paper reports simulation success rates of 90% for diagonal folding, 77% for hanging the towel, and 86% for folding to a tape mark. Real-world tests yielded success rates contingent on specific parameters like grasp accuracy and fold precision, revealing that agents could achieve substantial task execution without further real-world training.

Contributions and Insights

The contribution of this paper is two-fold: it extends end-to-end DRL strategies for rigid object manipulation to deformable objects, and it introduces a robust methodology for cloth manipulation tasks via sim-to-real transfer using domain randomization. The work is notable for integrating and evolving upon several techniques within the RL framework, such as the incorporation of behavior cloning and the Asymmetric Actor-Critic for improved training efficiency and policy robustness.

Implications

Practically, this research holds significant implications for robots operating in human-centric environments where interaction with deformable objects is frequent—encompassing home assistant robots, surgical robotics, and industrial automation. Theoretically, it advances our understanding of applying RL techniques to complex, variable environments and contributes to the field by emphasizing the importance and efficacy of simulation environments equipped with domain randomization.

Future Directions

Looking ahead, the work underlines the necessity of improved simulation environments for deformable objects, advocating for enhanced physics engines that can accurately replicate real-world deftness and fabric behaviors. Further exploration could include extending the framework to other classes of deformable objects, developing more sophisticated RL algorithms that reduce training time, or enhancing sim-to-real transfer fidelity possibly through improved sensor integration and more rigorous domain randomization strategies.

In conclusion, this paper substantiates the viability of employing DRL frameworks in manipulating deformable objects, marking a notable step towards more autonomous, adaptable robotic systems capable of operating efficiently in dynamic, unstructured settings.