PKU Value Lab Releases EmbodiedCLBench: First Benchmark for Continual Learning in Embodied Agents

Summary:PKU Value Lab has introduced EmbodiedCLBench, the first benchmark designed to evaluate the continual learning capabilities of self-evolving embodied agents. The benchmark, centered around compositional generalization, assesses whether agents can learn from basic tasks and recombine acquired skills to solve complex, unseen tasks. Experiments reveal that while advanced tasks are hard to solve in a zero-shot setting, experience from basic tasks leads to consistent improvements. However, this improv

---

Background and Motivation

Continual learning is a crucial area in AI that focuses on enabling models to accumulate past experiences and transfer them to new tasks. However, most existing research has been confined to non-embodied settings, with limited attention to embodied agents that operate in dynamic, complex environments. Embodied agents face higher demands for continual learning due to the need to handle tasks in such environments.

Design and Features of EmbodiedCLBench

EmbodiedCLBench, introduced by PKU Value Lab, is the first benchmark designed to evaluate the continual learning capabilities of embodied agents. Its core evaluation principle is compositional generalization, which assesses whether agents can learn skills from basic tasks and recombine them to solve more complex, unseen tasks.

Key features of the platform include:

1. Multi-task Support: Supports a variety of basic and complex task combinations, covering different types of embodied environments.
2. Dynamic Evaluation: Enables real-time assessment of agents' performance during task switching and experience accumulation.
3. Scalability: Supports the evaluation of various agent architectures and learning methods.

Experimental Results and Findings

Extensive experiments were conducted using EmbodiedCLBench, testing multiple agent architectures and learning methods. The main findings include:

• Zero-shot Learning Challenge: In a zero-shot setting, advanced tasks are nearly impossible to solve, highlighting the limitations of agents when facing entirely new tasks.
• Effectiveness of Experience Transfer: Experience from basic tasks leads to consistent performance improvements, even as task complexity increases.
• Bottleneck in Sample Efficiency: However, this improvement does not scale with the number of learning samples, revealing current limitations in leveraging additional experience.

Conclusion and Outlook

The release of EmbodiedCLBench fills a gap in the evaluation of continual learning for embodied agents, providing researchers with a standardized platform to test and compare different methods. The results indicate that while continual learning in embodied environments presents challenges, with proper task design and experience accumulation, agents can achieve significant performance improvements. Future research can utilize EmbodiedCLBench to explore more efficient continual learning methods and advance the application of embodied agents in the real world.

Developer Recommendations

• Leverage the Benchmark Platform: Developers are encouraged to use EmbodiedCLBench for testing and optimizing continual learning methods.
• Focus on Compositional Generalization: When designing agents, special attention should be paid to compositional generalization to improve performance in complex tasks.
• Explore New Learning Methods: Combine the findings from EmbodiedCLBench to explore new learning algorithms and architectures to overcome current bottlenecks in continual learning.

---

Source: Hugging Face Daily Papers (2026-10-09)

— END —

Tags: #Continual Learning Follow #Embodied Agents Follow #Benchmark Follow #PKU Value Lab Follow #Compositional Generalization Follow