AMOR: Adaptive Character Control through Multi-Objective Reinforcement Learning (2505.23708v1)

Abstract: Reinforcement learning (RL) has significantly advanced the control of physics-based and robotic characters that track kinematic reference motion. However, methods typically rely on a weighted sum of conflicting reward functions, requiring extensive tuning to achieve a desired behavior. Due to the computational cost of RL, this iterative process is a tedious, time-intensive task. Furthermore, for robotics applications, the weights need to be chosen such that the policy performs well in the real world, despite inevitable sim-to-real gaps. To address these challenges, we propose a multi-objective reinforcement learning framework that trains a single policy conditioned on a set of weights, spanning the Pareto front of reward trade-offs. Within this framework, weights can be selected and tuned after training, significantly speeding up iteration time. We demonstrate how this improved workflow can be used to perform highly dynamic motions with a robot character. Moreover, we explore how weight-conditioned policies can be leveraged in hierarchical settings, using a high-level policy to dynamically select weights according to the current task. We show that the multi-objective policy encodes a diverse spectrum of behaviors, facilitating efficient adaptation to novel tasks.

• The paper introduces AMOR, a multi-objective reinforcement learning framework that allows post-training reward weight adjustment to adapt character and robot behaviors efficiently.
• Numerical results show AMOR approximates the Pareto front well and its hierarchical extension improves performance over fixed-weight policies.
• This adaptive framework enables more resilient and flexible policy deployment in real-world robotics, facilitating future research into automated policy adjustment.

Adaptive Character Control through Multi-Objective Reinforcement Learning (AMOR)

The paper "AMOR: Adaptive Character Control through Multi-Objective Reinforcement Learning" introduces an innovative framework aimed at advancing the control of physics-based and robotic characters using reinforcement learning (RL). The prevalent methods in RL often rely on a weighted sum of conflicting reward functions, necessitating extensive tuning to achieve desired behaviors. This procedure is not only computationally expensive but also poses challenges when transferring the learned policies to real-world applications due to sim-to-real dynamics gaps. AMOR proposes a novel approach using multi-objective reinforcement learning (MORL) to address these limitations by allowing on-the-fly adaptability in reward weights post-training.

Key Contributions

1. Multi-Objective Framework: AMOR introduces a framework leveraging MORL to train a policy conditioned on a set of reward weights. This setup spans the Pareto front of reward trade-offs, enabling post-training selection and tuning of weights. This significantly reduces the iteration time required for behavioral adjustments.
2. Efficient Sim-to-Real Transfer: The framework allows manual and hierarchical adjustment of reward weights, facilitating the sim-to-real transfer of dynamic motions for robotic characters. Weight-conditioned policies provide flexibility in adapting behaviors efficiently to novel tasks.
3. Hierarchical Policy Integration: AMOR’s hierarchical extension employs a high-level policy (HLP) to dynamically select reward weights based on task requirements, thereby automating fine-grained selection of reward weights. This ensures efficient adaptation to novel tasks and offers interpretability of reward trade-offs.

Technical Details

AMOR’s training process uses a multi-objective variant of the Proximal Policy Optimization (PPO) algorithm, called MOPPO. The approach involves conditioning policies and rewards on a context vector that encodes task-relevant information and reward weights sampled from a multi-dimensional simplex. The policy is trained across diverse rewards such as joint-space and task-space tracking, velocity tracking, and smoothness. Conditioning on varied weights enables the extraction of a wide range of behaviors without requiring multiple retrainings for each weight configuration.

Numerical Results and Implications

The numerical results underscore AMOR’s capability to approximate the Pareto front closely for different motions, indicating robust adaptability to weight changes without necessitating retraining. Furthermore, the hierarchical policy demonstrated improved motion tracking performance by dynamically adjusting reward weights based on context, surpassing fixed-weight configurations in simulation scenarios.

Implications for Future Research:

• The adaptive framework presented in AMOR paves the way for more resilient and flexible policy deployment in real-world robotic applications, mitigating the drawbacks associated with sim-to-real discrepancies.
• The hierarchical approach raises intriguing possibilities for further research into automated policy adjustment mechanisms, potentially integrating more sophisticated real-time feedback systems or sensor input for dynamic adaptation.
• This work lays the groundwork for expanding adaptive systems beyond character control, including potential applications in complex robotic and autonomous systems requiring real-time adaptability and learning efficiency.

AMOR represents a significant leap forward in the field of learning-based character control, advocating for a paradigm shift towards more dynamic and adaptable policy frameworks in reinforcement learning and robotics applications.