Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation

2025/05/15 by Xinrui Wang, Yan Jin, Wang, Xinrui +1
Computer Science · Engineering · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Intelligent Tutoring Systems and Adaptive Learning #Machine Learning (cs.LG) #Robot Manipulation and Learning #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2505.10522

openalex publication_date 2025/05/15 · openalex created_date 2025/10/15 · openalex updated_date 2026/07/28

Abstract

Reinforcement learning (RL) has demonstrated remarkable potential in robotic manipulation but faces challenges in sample inefficiency and lack of interpretability, limiting its applicability in real world scenarios. Enabling the agent to gain a deeper understanding and adapt more efficiently to diverse working scenarios is crucial, and strategic knowledge utilization is a key factor in this process. This paper proposes a Knowledge Capture, Adaptation, and Composition (KCAC) framework to systematically integrate knowledge transfer into RL through cross-task curriculum learning. KCAC is evaluated using a two block stacking task in the CausalWorld benchmark, a complex robotic manipulation environment. To our knowledge, existing RL approaches fail to solve this task effectively, reflecting deficiencies in knowledge capture. In this work, we redesign the benchmark reward function by removing rigid constraints and strict ordering, allowing the agent to maximize total rewards concurrently and enabling flexible task completion. Furthermore, we define two self-designed sub-tasks and implement a structured cross-task curriculum to facilitate efficient learning. As a result, our KCAC approach achieves a 40 percent reduction in training time while improving task success rates by 10 percent compared to traditional RL methods. Through extensive evaluation, we identify key curriculum design parameters subtask selection, transition timing, and learning rate that optimize learning efficiency and provide conceptual guidance for curriculum based RL frameworks. This work offers valuable insights into curriculum design in RL and robotic learning.

Citations

• CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models
• Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models
• Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology
• Exploring CausalWorld: Enhancing robotic manipulation via knowledge transfer and curriculum learning
• R³: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics
• Robust Multi-Agent Reinforcement Learning with State Uncertainty
• Proximal Curriculum for Reinforcement Learning Agents
• A Data-Centric Solution to NonHomogeneous Dehazing via Vision Transformer
• CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and\n Transfer Learning
• Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
• Continuous control with deep reinforcement learning

Related

• Exploring CausalWorld: Enhancing robotic manipulation via knowledge transfer and curriculum learning
• MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning
• CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and\n Transfer Learning
• Learning Multi-Objective Curricula for Robotic Policy Learning
• Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Study