Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation (2505.10522v1)

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.

Overview of KCAC Framework: A Novel Approach to Cross-Task Curriculum Learning in Robotic Manipulation

The paper presents a framework named Knowledge Capture, Adaptation, and Composition (KCAC) developed to enhance the efficiency and interpretability challenges often encountered in reinforcement learning (RL) applied to robotic manipulation tasks. RL has shown significant potential in solving complex robotic tasks but is often limited by sample inefficiency and interpretability issues—barriers that render its real-world application challenging. The KCAC framework adopts a curriculum learning approach across tasks, offering a systematic methodology for integrating knowledge transfer into RL processes.

The authors use a two-block stacking task within the CausalWorld benchmark to evaluate the KCAC framework. Existing RL approaches have struggled significantly with this task, demonstrating deficiencies in knowledge capture. To address these limitations, the paper suggests a redesign of the reward function used in the benchmark. By removing strict ordering in the learning process and rigid constraints often found in task completion, the reformulated reward function allows concurrent maximization of various components of task completion.

Upon implementation, the KCAC approach yields noteworthy improvements: a 40% reduction in training time alongside a 10% improvement in task success rate compared to traditional RL methods. This is achieved by introducing self-designed sub-tasks and applying a structured cross-task curriculum learning strategy, which effectively decomposes the complex task into manageable segments.

Key Findings and Implications

The KCAC framework demonstrates several important insights into RL's curriculum-based learning processes, particularly within robotic manipulation contexts.

1. Reward Function Design: The paper emphasizes the necessity of flexible reward structures, which encourage incremental progress and task completion rather than enforcing a rigid learning sequence. The re-engineered reward function in this paper lays the groundwork for optimizing RL agents' learning efficiency in dynamic and complex environments.
2. Knowledge Transfer Parameters: The paper's analysis of transition timing and learning rates in curriculum learning reveals significant insights. Low similarity between tasks requires high learning rates and early transitions for effective knowledge transfer, while higher task similarity allows for fine-tuning at lower learning rates and benefits from longer pre-training durations.
3. Curriculum Complexity: By developing three-stage curricula, the paper illustrates how multi-stage learning designs can outperform simpler curricula, thereby reducing task learning time significantly. This approach highlights the necessity of decomposing complex real-world tasks into achievable learning segments for RL agents.

Potential for Future AI Developments

The KCAC framework contributes a structured approach to integrating knowledge transfer and curriculum learning processes within RL for robotic manipulation tasks. Future research directions can explore the incorporation of additional sub-tasks to diversify learning complexities further. Additionally, the framework's generalizability across different robotic tasks and engineering domains remains an area ripe for exploration.

Lastly, advancements in machine learning that improve task-similarity measurements and tuning of transition parameters could enhance the KCAC framework’s robustness and applicability, offering strategic insights into refining RL-based tasks in real-world applications. Overall, KCAC provides a promising avenue for further exploration in the domain of curriculum learning and knowledge transfer in AI.