The field of reinforcement learning and soft robotics is rapidly evolving, with a focus on developing robust and adaptable systems. Recent research has explored the integration of control contraction metrics into reinforcement learning, enabling the creation of policies that are both stable and optimal. Additionally, there has been a shift towards designing systems that can operate effectively in uncertain and dynamic environments, such as those with deformable obstacles. Soft robotics has also seen significant advancements, with the development of novel materials and control strategies that enable improved adaptability and dexterity. Notable papers include the proposal of a contraction actor-critic algorithm, which enhances the capability of control contraction metrics to provide a set of contracting policies with the long-term optimality of reinforcement learning. Another noteworthy paper introduces an antifragile reinforcement learning framework that incorporates a switching mechanism based on discounted Thompson sampling, enabling the system to adapt to evolving adversarial strategies.
Advancements in Robust Reinforcement Learning and Soft Robotics
Sources
Contraction Actor-Critic: Contraction Metric-Guided Reinforcement Learning for Robust Path Tracking
Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation
A Survey on Soft Robot Adaptability: Implementations, Applications, and Prospects
Soft Robotic Delivery of Coiled Anchors for Cardiac Interventions
Adversarial Observability and Performance Tradeoffs in Optimal Control
Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation
Robust Policy Switching for Antifragile Reinforcement Learning for UAV Deconfliction in Adversarial Environments
Curriculum-Guided Antifragile Reinforcement Learning for Secure UAV Deconfliction under Observation-Space Attacks