- The paper introduces a CMORL framework, CoMOPPO, applying stage-wise reward shaping to simplify control optimization for complex robotic acrobatics.
- The method is validated in simulation and real-world on a Unitree Go1 robot performing tasks like back-flips, demonstrating successful sim-to-real transfer.
- CoMOPPO outperforms existing methods, achieving higher success rates on multi-stage tasks and providing a scalable framework for broader robotic applications beyond acrobatics.
Stage-Wise Reward Shaping for Acrobatic Robots: A Constrained Multi-Objective Reinforcement Learning Approach
The paper, authored by Dohyeong Kim et al., explores the challenges of reward shaping in reinforcement learning (RL) for acrobatic robot tasks, introducing a novel methodology that operates within a constrained multi-objective RL (CMORL) framework. This approach aims to simplify and enhance the reward-shaping process for complex robotic tasks that involve sequential movements, such as back-flips and two-hand walks, by defining multiple rewards and cost functions linked to distinct stages of a task.
In the current landscape, managing the complexity of reward functions for tasks that must balance competing demands such as performance, safety, and energy efficiency is a significant challenge. Traditional methods tend to amalgamate various elements into a single reward function, which complicates the tuning and can be labor-intensive. Instead, the authors propose segmenting complex tasks into stages and applying separate reward and cost functions to each stage. This stage-wise reward shaping not only simplifies the formulation of rewards but also aligns more closely with the evolving requirements of acrobatic maneuvers.
The core contribution of this work is twofold: First, it introduces a CMORL algorithm that incorporates these multi-stage reward and cost definitions. This algorithm, dubbed constrained multi-objective PPO (CoMOPPO), builds upon the proximal policy optimization (PPO) approach by adapting it to aggregate multiple objectives and constraints effectively. CoMOPPO utilizes techniques such as reward normalization and standard deviation adjustments to ensure a balanced consideration of multiple objectives and constraints during policy updates.
Second, the paper demonstrates the applicability and success of the proposed method through various robotic tasks, both in simulated and real-world environments. Tasks such as back-flips, side-rolls, and two-hand walks were used to showcase the method's efficacy. Experimental results indicate that CoMOPPO can optimize policies in a manner that is robust to various objective trade-offs and performance constraints, a notable improvement over traditional single-objective or constrained RL techniques.
Notably, the approach supports sim-to-real transfers, a critical feature for real-world applications, by leveraging techniques such as domain randomization and teacher-student learning to bridge the gap between simulated training and physical deployment. The real-world demonstrations using the Unitree Go1 quadrupedal robot further validate this method's potential for broader application in flexible and dynamic robot control.
In the conducted ablation studies, CoMOPPO showed a superior ability to handle complex acrobatic tasks compared to existing RL and constrained RL algorithms. It achieved a higher success rate in completing specified tasks across multiple stages, with more successful transitions between these stages.
The implications of this research are significant in both theoretical and practical dimensions. By framing the reward problem in a CMORL context, the authors provide a scalable framework that could be adopted for a wide range of robotic applications beyond acrobatics. Theoretically, this work prompts further exploration into autonomous stage segmentation and reward formulation for more diverse multi-step robotic tasks. Future research could also investigate adaptive methods for real-time dynamic task segmentation, potentially enhancing autonomous robotic capabilities in more complex and dynamic environments.