A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation
The rise of generalist large-scale models in natural language and vision has made us expect that a massive data-driven approach could achieve broader generalization in other domains such as continuous control. In this work, we explore a method for learning a single policy that manipulates various forms of agents to solve various tasks by distilling a large amount of proficient behavioral data. In order to align input-output (IO) interface among multiple tasks and diverse agent morphologies while preserving essential 3D geometric relations, we introduce morphology-task graph, which treats observations, actions and goals/task in a unified graph representation. We also develop MxT-Bench for fast large-scale behavior generation, which supports procedural generation of diverse morphology-task combinations with a minimal blueprint and hardware-accelerated simulator. Through efficient representation and architecture selection on MxT-Bench, we find out that a morphology-task graph representation coupled with Transformer architecture improves the multi-task performances compared to other baselines including recent discrete tokenization, and provides better prior knowledge for zero-shot transfer or sample efficiency in downstream multi-task imitation learning. Our work suggests large diverse offline datasets, unified IO representation, and policy representation and architecture selection through supervised learning form a promising approach for studying and advancing morphology-task generalization.
Code (1)
Tasks
continuous-controlContinuous ControlImitation LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Graph-Operator World Models for Morphology-Parameter Generalization in Continuous Control
World models for continuous control are commonly trained for a fixed physical system and can degrade when known morphology parameters such as link lengths, masses, damping, and actuation change. Existing approaches often…
Continuous ControlMorphGen: Morphology-Guided Representation Learning for Robust Single-Domain Generalization in Histopathological Cancer Classification
Domain generalization in computational histopathology is hindered by heterogeneity in whole slide images (WSIs), caused by variations in tissue preparation, staining, and imaging conditions across institutions. Unlike ma…
Representation LearningDomain GeneralizationCancer ClassificationNuclear SegmentationAnyMorph: Learning Transferable Polices By Inferring Agent Morphology
The prototypical approach to reinforcement learning involves training policies tailored to a particular agent from scratch for every new morphology. Recent work aims to eliminate the re-training of policies by investigat…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Zero-shot GeneralizationModality vs. Morphology: A Framework for Time Series Classification for Biological Signals
Time series classification (TSC) of biological signals has progressed from handcrafted, modality-specific approaches to deep architectures capable of representing the diverse waveform structures of underlying physiologic…
Time Series ClassificationData AugmentationLearning to Get Up Across Morphologies: Zero-Shot Recovery with a Unified Humanoid Policy
Fall recovery is a critical skill for humanoid robots in dynamic environments such as RoboCup, where prolonged downtime often decides the match. Recent techniques using deep reinforcement learning (DRL) have produced rob…
Zero-shot GeneralizationReinforcement Learning