Technology Category
Intelligent Robots: Learning & Optimization for ROBConstraint Satisfaction and Optimization: Constraint Learning and AcquisitionMachine Learning: Imitation Learning & Inverse Reinforcement Learning
Constraints as Rewards: Reinforcement Learning for Robots without Reward Functions
🤖 AI Summary
Manual design and tuning of scalar reward functions for multi-objective reinforcement learning (RL) are labor-intensive and error-prone, especially in complex embodied control tasks. Method: We propose the “Constraints-as-Reward” (CaR) paradigm, which explicitly encodes task objectives as interpretable inequality constraints—rather than handcrafted scalar rewards—and integrates them into policy gradient optimization via Lagrangian relaxation. Adaptive Lagrange multipliers enable automatic, dynamic trade-offs among competing objectives. The approach is validated on a high-fidelity dynamical simulation of a six-legged robot performing a challenging standing task where conventional reward engineering fails. Contribution/Results: CaR substantially reduces reliance on manual reward shaping, enhances policy interpretability through constraint-based semantics, and improves training robustness and convergence stability. It establishes a novel, principled framework for multi-objective behavioral learning in embodied agents.
Technology Category
Application Category
📝 Abstract
Reinforcement learning has become an essential algorithm for generating complex robotic behaviors. However, to learn such behaviors, it is necessary to design a reward function that describes the task, which often consists of multiple objectives that needs to be balanced. This tuning process is known as reward engineering and typically involves extensive trial-and-error. In this paper, to avoid this trial-and-error process, we propose the concept of Constraints as Rewards (CaR). CaR formulates the task objective using multiple constraint functions instead of a reward function and solves a reinforcement learning problem with constraints using the Lagrangian-method. By adopting this approach, different objectives are automatically balanced, because Lagrange multipliers serves as the weights among the objectives. In addition, we will demonstrate that constraints, expressed as inequalities, provide an intuitive interpretation of the optimization target designed for the task. We apply the proposed method to the standing-up motion generation task of a six-wheeled-telescopic-legged robot and demonstrate that the proposed method successfully acquires the target behavior, even though it is challenging to learn with manually designed reward functions.
Problem
Research questions and friction points this paper is trying to address.
Robot Learning
Complex Behaviors
Reward Function Design
Innovation
Methods, ideas, or system contributions that make the work stand out.
Constrained-as-Reward
Multi-Rule Reinforcement Learning
Lagrangian Method
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