Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks (1906.08880v2)

Abstract: Reinforcement Learning (RL) of contact-rich manipulation tasks has yielded impressive results in recent years. While many studies in RL focus on varying the observation space or reward model, few efforts focused on the choice of action space (e.g. joint or end-effector space, position, velocity, etc.). However, studies in robot motion control indicate that choosing an action space that conforms to the characteristics of the task can simplify exploration and improve robustness to disturbances. This paper studies the effect of different action spaces in deep RL and advocates for Variable Impedance Control in End-effector Space (VICES) as an advantageous action space for constrained and contact-rich tasks. We evaluate multiple action spaces on three prototypical manipulation tasks: Path Following (task with no contact), Door Opening (task with kinematic constraints), and Surface Wiping (task with continuous contact). We show that VICES improves sample efficiency, maintains low energy consumption, and ensures safety across all three experimental setups. Further, RL policies learned with VICES can transfer across different robot models in simulation, and from simulation to real for the same robot. Further information is available at https://stanfordvl.github.io/vices.

• The paper demonstrates that VICES offers superior sample efficiency and task completion in contact-rich robot manipulation compared to traditional fixed impedance methods.
• It introduces a hybrid action space that dynamically adjusts both motion and compliance for optimal energy utilization in diverse tasks.
• The study confirms improved transferability across different robotic setups and environments, underscoring VICES' practical applicability.

Insights on Variable Impedance Control in End-Effector Space for Reinforcement Learning in Contact-Rich Tasks

The paper under discussion presents an insightful evaluation of Reinforcement Learning (RL) strategies for robot manipulation tasks with an emphasis on the selection of action spaces, particularly focusing on contact-rich environments. The researchers aim to expand the domain of RL beyond the oft-explored observation spaces and reward models, providing a nuanced examination of how different action spaces impact the efficacy of learned policies in robotic tasks.

Methodological Advancement: VICES

Central to the discourse is the introduction and advocacy of Variable Impedance Control in End-Effector Space (VICES). Prior research in robot motion control has demonstrated the benefits of impedance control in adjusting a robot's compliance to real-world interactions, yet this paper argues for dynamic adjustment of both motion and impedance in the same space for improved efficiency in tasks with varying physical constraints. The paper compares action spaces such as joint positions, joint velocities, and fixed impedance in task space to assess the proposed VICES model's practical and theoretical implications.

Evaluation Dimensions

To investigate the benefits of VICES, the authors conducted empirical evaluations across three distinct manipulation scenarios: Path Following, Door Opening, and Surface Wiping. These were chosen to represent different levels of task-space constraints and interactions - from no contact to continuous contact tasks. A noteworthy aspect of the paper design is the two-fold evaluation matrix focusing on both physical efficiency (e.g., energy consumption and applied forces) and transferability (cross-robot and sim-to-real). These dual focuses reflect a holistic approach to assessing policy robustness and real-world applicability.

Significant Findings

The numerical results obtained underscore the advantages of variable impedance in end-effector space:

• Sample Efficiency and Task Completion: VICES consistently emerged as the most sample-efficient mode across tasks, maintaining high levels of reward and achieving successful task completion, especially in contact-rich scenarios.
• Energy Consumption: Robots leveraging VICES achieved lower energy expenditure due to the ability to dynamically adjust stiffness and damping based on task requirements. This was particularly evident in the surface wiping task where maintaining optimal force application is critical to performance.
• Transferability: VICES facilitated seamless policy transfer between different robotic configurations and between simulated and real-world environments, highlighting its capability for generalization beyond the initial training conditions.

Theoretical Implications and Future Directions

The paper contributes substantially to action space theory within the RL paradigms, suggesting that an action space that inherently includes both motion and compliance parameters (VICES) can significantly improve policy learning and application in complex tasks. This insight opens potential avenues for refining robotic control systems to adapt fluidly to unpredictable environments without human intervention.

For future developments, the findings invite further exploration into synergistic action-observation space configurations and their impact on complex, high-dimensional tasks. Moreover, incorporating autonomously learned task heuristics into variable impedance control could enhance adaptability even further, fostering more autonomous robotic systems capable of efficiently handling the diversity of real-world task constraints.

In summary, the paper delineates a critical advancement in RL application by demonstrating that the action space's nature fundamentally influences policy success in contact-rich task scenarios. Variable Impedance Control in End-Effector Space emerges not only as a preferred choice for these tasks but also as a template for exploring other hybrid action paradigms.