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Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot

Xinda Qi, Dong Chen, Zhaojian Li, Xiaobo Tan

TL;DR

This work tackles sample-inefficient learning in off-policy reinforcement learning for soft robots by introducing Back-stepping Experience Replay (BER), a bidirectional exploration framework that uses approximate reversibility to generate reversed trajectories via back-stepping transitions. BER maintains separate replay buffers for forward and back-stepping experiences and uses a probabilistic sampling scheme with $P_{t,f}$ and $P_{t,b}$, with reversibility bounded by $\lVert \bm{s}_{b,t}-\bm{s}_t \rVert \le K \lVert \bm{s}_{t+1}-\bm{s}_t \rVert$ and $K<1$, decaying the backward component when needed. The algorithm is validated on a toy binary bit-flipping task and then applied to a model-free RL problem controlling a soft snake robot performing serpentine locomotion, using a compact four-path actuation model and a target-conditioned state space. Experiments show BER improves learning speed and stability, achieving $100\%$ success on random targets and a $48\%$ increase in average speed compared to the best baseline, underscoring BER's potential to enhance data efficiency in off-policy RL for soft robotics. The results suggest BER's bidirectional search and distillation of transitional information can generalize to other reversible or approximately reversible dynamical systems and tasks beyond soft robotics.

Abstract

In this paper, we propose a novel technique, Back-stepping Experience Replay (BER), that is compatible with arbitrary off-policy reinforcement learning (RL) algorithms. BER aims to enhance learning efficiency in systems with approximate reversibility, reducing the need for complex reward shaping. The method constructs reversed trajectories using back-stepping transitions to reach random or fixed targets. Interpretable as a bi-directional approach, BER addresses inaccuracies in back-stepping transitions through a distillation of the replay experience during learning. Given the intricate nature of soft robots and their complex interactions with environments, we present an application of BER in a model-free RL approach for the locomotion and navigation of a soft snake robot, which is capable of serpentine motion enabled by anisotropic friction between the body and ground. In addition, a dynamic simulator is developed to assess the effectiveness and efficiency of the BER algorithm, in which the robot demonstrates successful learning (reaching a 100% success rate) and adeptly reaches random targets, achieving an average speed 48% faster than that of the best baseline approach.

Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot

TL;DR

This work tackles sample-inefficient learning in off-policy reinforcement learning for soft robots by introducing Back-stepping Experience Replay (BER), a bidirectional exploration framework that uses approximate reversibility to generate reversed trajectories via back-stepping transitions. BER maintains separate replay buffers for forward and back-stepping experiences and uses a probabilistic sampling scheme with and , with reversibility bounded by and , decaying the backward component when needed. The algorithm is validated on a toy binary bit-flipping task and then applied to a model-free RL problem controlling a soft snake robot performing serpentine locomotion, using a compact four-path actuation model and a target-conditioned state space. Experiments show BER improves learning speed and stability, achieving success on random targets and a increase in average speed compared to the best baseline, underscoring BER's potential to enhance data efficiency in off-policy RL for soft robotics. The results suggest BER's bidirectional search and distillation of transitional information can generalize to other reversible or approximately reversible dynamical systems and tasks beyond soft robotics.

Abstract

In this paper, we propose a novel technique, Back-stepping Experience Replay (BER), that is compatible with arbitrary off-policy reinforcement learning (RL) algorithms. BER aims to enhance learning efficiency in systems with approximate reversibility, reducing the need for complex reward shaping. The method constructs reversed trajectories using back-stepping transitions to reach random or fixed targets. Interpretable as a bi-directional approach, BER addresses inaccuracies in back-stepping transitions through a distillation of the replay experience during learning. Given the intricate nature of soft robots and their complex interactions with environments, we present an application of BER in a model-free RL approach for the locomotion and navigation of a soft snake robot, which is capable of serpentine motion enabled by anisotropic friction between the body and ground. In addition, a dynamic simulator is developed to assess the effectiveness and efficiency of the BER algorithm, in which the robot demonstrates successful learning (reaching a 100% success rate) and adeptly reaches random targets, achieving an average speed 48% faster than that of the best baseline approach.
Paper Structure (15 sections, 8 equations, 10 figures, 1 table)

This paper contains 15 sections, 8 equations, 10 figures, 1 table.

Table of Contents

  1. Introduction
  2. Back-stepping Experience Replay
  3. Background
  4. Reinforcement Learning
  5. Deep Q-Networks (DQN) and Deep Deterministic Policy Gradient (DDPG)
  6. Algorithm for BER
  7. A case study of BER
  8. BER in Model-free RL for a Soft Snake Robot
  9. Soft Snake Robot and Serpentine Locomotion
  10. RL formulation of Locomotion and Navigation of the Robot
  11. Experiments of RL algorithms
  12. Experimental Setups
  13. Locomotion and Navigation with a Fixed Target
  14. Locomotion and Navigation with Random Targets
  15. Conclusions and Discussions

Figures (10)

  • Figure 1: Illustration of the Back-stepping Experience Replay.
  • Figure 2: Training experiments of the bit flip game with different algorithms and state dimensions. (A) Returns; (B) Success rates.
  • Figure 3: The overview of the soft snake robot with skins. (A) The soft snake robot with soft snakeskins; (B) The connection between air chambers and air paths; (C) The actuation pressures for air paths; (D) The structure of one bending actuator; (E) The structure of soft snakeskin; (F) The simulation (sim) and experimental (exp) results of the trajectory of the COM of the snake robot on a rough paper surface.
  • Figure 4: The illustration of the soft snake robot with serpentine locomotion approaching a target.
  • Figure 5: (A). The approximate reversibility of the movement of the soft snake robot with snake skins. (B). The fixed target and the sampling range of the random targets.
  • ...and 5 more figures