Know your limits! Optimize the robot's behavior through self-awareness
As humanoid robots transition from labs to real-world environments, it is essential to democratize robot control for non-expert users. Recent human-robot imitation algorithms focus on following a reference human motion with high precision, but they are susceptible to the quality of the reference motion and require the human operator to simplify its movements to match the robot's capabilities. Instead, we consider that the robot should understand and adapt the reference motion to its own abilities, facilitating the operator's task. For that, we introduce a deep-learning model that anticipates the robot's performance when imitating a given reference. Then, our system can generate multiple references given a high-level task command, assign a score to each of them, and select the best reference to achieve the desired robot behavior. Our Self-AWare model (SAW) ranks potential robot behaviors based on various criteria, such as fall likelihood, adherence to the reference motion, and smoothness. We integrate advanced motion generation, robot control, and SAW in one unique system, ensuring optimal robot behavior for any task command. For instance, SAW can anticipate falls with 99.29% accuracy. For more information check our project page: https://evm7.github.io/Self-AWare
Code (0)
등록된 구현이 없습니다.
Tasks
Motion GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion
Deep reinforcement learning (RL) can enable robots to autonomously acquire complex behaviors, such as legged locomotion. However, RL in the real world is complicated by constraints on efficiency, safety, and overall trai…
Deep Reinforcement LearningEfficient ExplorationReinforcement Learning (RL)Your Robot Will Feel You Now: Empathy in Robots and Embodied Agents
The fields of human-robot interaction (HRI) and embodied conversational agents (ECAs) have long studied how empathy could be implemented in machines. One of the major drivers has been the goal of giving multimodal social…
Emotional IntelligenceA Robot to Shape your Natural Plant: The Machine Learning Approach to Model and Control Bio-Hybrid Systems
Bio-hybrid systems---close couplings of natural organisms with technology---are high potential and still underexplored. In existing work, robots have mostly influenced group behaviors of animals. We explore the possibili…
Decision MakingOptimizing Neurorobot Policy under Limited Demonstration Data through Preference Regret
Robot reinforcement learning from demonstrations (RLfD) assumes that expert data is abundant; this is usually unrealistic in the real world given data scarcity as well as high collection cost. Furthermore, imitation lear…
Reinforcement LearningKnow Your Limits: A Survey of Abstention in Large Language Models
Abstention, the refusal of large language models (LLMs) to provide an answer, is increasingly recognized for its potential to mitigate hallucinations and enhance safety in LLM systems. In this survey, we introduce a fram…