Never-Ending Embodied Robot Learning: A Study on NBCagent for Continual Learning

Kernekoncepter

The author explores the challenges of never-ending embodied robot learning and proposes the NBCagent as a solution to continually learn novel skills while addressing old skill forgetting.

Resumé

The study investigates the challenges faced by embodied robots in continual learning. The NBCagent is introduced as a language-conditioned agent that can learn novel manipulation skills without forgetting old ones. By decoupling skill-specific and skill-shared knowledge, the agent effectively tackles catastrophic forgetting. Experimental results demonstrate the significant performance of the proposed method.
Relying on large language models, embodied robots can perform complex tasks from visual observations with generalization ability. However, existing methods face degradation in manipulation performance and knowledge forgetting when adapting to new tasks. The NBCagent addresses this challenge by continually learning observation knowledge of novel skills through skill-specific and skill-shared attributes. The study establishes a skill-specific evolving planner, semantics rendering module, and representation distillation module to transfer anti-forgetting knowledge.
Continual learning aims to acquire knowledge from successive data streams and has shown remarkable performance in various computer vision tasks. The NERL problem introduces incremental tasks where agents must counteract catastrophic forgetting from old skills while acquiring new ones continuously. The study proposes a novel approach with the NBCagent to address these challenges effectively.

Statistik

Most visual behavior-cloning agents suffer from manipulation performance degradation.
Existing methods assume training on a fixed dataset.
Proposed method demonstrates significant performance in experiments.
NERL problem involves continual learning on successive behavior-cloning manipulation skills.
Skill-specific evolving planner decouples latent space for knowledge acquisition.
Skill-shared semantics rendering module transfers shared knowledge effectively.
Skill-shared representation distillation module aligns shared representation between old and current models.

Citater

"Embodied robot learning has attracted interest in merging machine learning with robot control systems."
"NBCagent can continually learn observation knowledge of novel robot manipulation skills."
"Our proposed method addresses catastrophic forgetting on old skills."

Vigtigste indsigter udtrukket fra

Never-Ending Embodied Robot Learning

by Wenqi Liang,... kl. arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00336.pdf

Dybere Forespørgsler

How does the NBCagent compare to other state-of-the-art methods in robotic manipulation

The NBCagent outperforms other state-of-the-art methods in robotic manipulation by addressing the challenges of never-ending embodied robot learning. Compared to traditional methods like PerAct and GNFactor, the NBCagent continually learns skill-wise knowledge through a skill-specific evolving planner (SEP) and skill-shared semantics rendering module (SSR). This allows the agent to effectively learn novel skills while mitigating catastrophic forgetting on old skills. The results show that the NBCagent achieves higher success rates on both base tasks and incremental tasks, demonstrating its robustness and performance in handling complex manipulation tasks.

What are the potential real-world applications of never-ending embodied robot learning

Never-ending embodied robot learning has significant real-world applications across various industries. One potential application is in industrial automation, where robots need to continually adapt to new manufacturing processes or tasks without forgetting previously learned skills. In healthcare, never-ending embodied robot learning can be used for medical assistance robots that continuously acquire new capabilities while retaining essential medical procedures they have learned over time. Additionally, in home robotics, this approach enables robots to perform a wide range of household chores efficiently by continually acquiring new skills based on user requirements.

How can the concept of continual learning be applied to other fields beyond robotics

The concept of continual learning can be applied beyond robotics to various fields such as natural language processing (NLP), computer vision, and autonomous vehicles. In NLP, models can benefit from continual learning by adapting to changing language patterns and incorporating new vocabulary seamlessly. For computer vision applications, continual learning allows models to adapt to different visual environments or object categories without forgetting previous knowledge. In autonomous vehicles, continual learning ensures that self-driving cars can continuously improve their driving capabilities based on real-world data while maintaining safety standards established during initial training phases.