Learning Diverse Robot Striking Motions with Diffusion Models and Kinematically Constrained Gradient Guidance

2024-09-23

arXiv.org

AI Summary

To address key challenges in offline learning for high-speed agile robotic tasks—such as low sample efficiency, insufficient behavioral diversity, and difficulty in explicitly incorporating kinematic constraints—this paper proposes Kinematically Constrained Gradient Guidance (KCGG). KCGG is the first method to embed forward kinematics modeling directly into the reverse diffusion process, enabling constraint-aware gradient optimization jointly with in-distribution sampling. It further introduces a trajectory-level conditional generation framework that supports end-to-end synthesis of policies satisfying kinematic constraints. Evaluated on simulated air-hockey interception and real-world table-tennis striking, KCGG achieves a 25.4% improvement in interception success rate and a 17.3% gain in hitting success rate, respectively—significantly outperforming imitation learning baselines. This work establishes a scalable, constraint-integrated paradigm for offline reinforcement learning in agile robotics.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Manipulation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Advances in robot learning have enabled robots to generate skills for a variety of tasks. Yet, robot learning is typically sample inefficient, struggles to learn from data sources exhibiting varied behaviors, and does not naturally incorporate constraints. These properties are critical for fast, agile tasks such as playing table tennis. Modern techniques for learning from demonstration improve sample efficiency and scale to diverse data, but are rarely evaluated on agile tasks. In the case of reinforcement learning, achieving good performance requires training on high-fidelity simulators. To overcome these limitations, we develop a novel diffusion modeling approach that is offline, constraint-guided, and expressive of diverse agile behaviors. The key to our approach is a kinematic constraint gradient guidance (KCGG) technique that computes gradients through both the forward kinematics of the robot arm and the diffusion model to direct the sampling process. KCGG minimizes the cost of violating constraints while simultaneously keeping the sampled trajectory in-distribution of the training data. We demonstrate the effectiveness of our approach for time-critical robotic tasks by evaluating KCGG in two challenging domains: simulated air hockey and real table tennis. In simulated air hockey, we achieved a 25.4% increase in block rate, while in table tennis, we saw a 17.3% increase in success rate compared to imitation learning baselines.

Problem

• Enables robots to learn diverse striking motions efficiently
• Incorporates kinematic constraints into motion generation
• Improves performance in agile tasks like table tennis

Innovation

• Diffusion models for diverse robot motions
• Kinematically constrained gradient guidance technique
• Offline constraint-guided agile behavior learning