Introducing BOSS (Bootstrapping your own SkillS): a revolutionary approach that leverages large language models to autonomously create a versatile skills library to tackle complex tasks with minimal guidance. Compared to conventional unsupervised skill acquisition techniques and simplistic priming methods, BOSS performs better in performing unfamiliar tasks in novel environments. This innovation marks a significant advance in the independent acquisition and application of skills.
Reinforcement learning seeks to optimize policies in Markov decision-making processes to maximize expected returns – previous RL research has pre-trained reusable skills for complex tasks. Unsupervised RL, focused on curiosity, controllability and diversity, skills learned without human input. The language was used for skill setting and open-loop planning. BOSS extends skill repertoires with large language models, guiding exploration and rewarding completion of the skill chain, thereby producing higher success rates in long-term task execution.
Traditional robot learning relies largely on supervision, while humans excel at independently learning complex tasks. The researchers presented BOSS as a framework for learning various long-term skills with minimal human intervention in a self-paced manner. Through skill priming and guided by large language models (LLM), BOSS gradually builds and combines skills to handle complex tasks. Unsupervised environmental interactions improve the robustness of its policy for solving difficult tasks in new environments.
BOSS introduces a two-phase framework. In the first phase, he acquires a set of fundamental skills using unsupervised RL objectives. The second phase, skill priming, uses LLMs to guide skill sequencing and rewards based on skill completion. This approach allows agents to build complex behaviors from basic skills. Experiments in home environments show that LLM-guided bootstrapping outperforms naive bootstrapping and previous unsupervised methods in performing long-term unknown tasks in novel contexts.
Experimental results confirm that BOSS, guided by LLMs, excels in solving extensive housekeeping tasks in novel contexts, outperforming LLM-based planning and unsupervised exploration methods. Results present inter-quartile means and standard deviations of Oracle-normalized achievements and Oracle-normalized pass rates for tasks of varying duration in the ALFRED assessments. Agents trained in LLM-guided bootstrapping outperform naive bootstrapping and previous unsupervised methods. BOSS can autonomously acquire diverse and complex behaviors from basic skills, demonstrating its potential for expert-less robotic skill acquisition.
The BOSS framework, guided by LLMs, excels in solving complex tasks autonomously without the help of an expert. Agents trained in LLM-guided bootstrap outperform naive bootstrap and prior unsupervised methods when performing unknown functions in novel environments. Realistic home experiments confirm the effectiveness of BOSS in acquiring diverse and complex behaviors from basic skills, thus highlighting its potential for autonomous acquisition of robotics skills. BOSS also shows promise in linking reinforcement learning to natural language understanding, using pre-trained language models for guided learning.
Future research directions could include:
- Investigating zero-reset RL for self-directed skill learning.
- Propose a long-term distribution of tasks with the BOSS skills chain approach.
- Extending unsupervised RL for low-level skill acquisition.
Improving the integration of reinforcement learning with natural language understanding in the BOSS framework is also a promising direction. Applying BOSS to various domains and evaluating its performance in various environments and task contexts offers potential for further exploration.
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Hello, My name is Adnan Hassan. I’m a consulting intern at Marktechpost and soon to be a management intern at American Express. I am currently pursuing a dual degree at Indian Institute of Technology, Kharagpur. I am passionate about technology and want to create new products that make a difference.