Inferring the Geometric Nullspace of Robot Skills from Human Demonstrations

TReF-6：从单次演示推断任务相关坐标系以实现一次性技能泛化

TReF-6: Inferring Task-Relevant Frames from a Single Demonstration for One-Shot Skill Generalization

本文提出 TReF-6，从单条轨迹几何推断 6DoF 任务相关坐标系并结合 DMP、视觉—语言模型与 Grounded-SAM，实现跨场景、保持任务意图的一次性机器人操作技能泛化。

Visual Geometric Skill Inference by Watching Human Demonstration

We study the problem of learning manipulation skills from human demonstration video by inferring the association relationships between geometric features. Motivation for this work stems from the observation that humans perform eye-hand coordination tasks by using geometric primitives to define a task while a geometric control error drives the task through execution. We propose a graph based kernel regression method to directly infer the underlying association constraints from human demonstration video using Incremental Maximum Entropy Inverse Reinforcement Learning (InMaxEnt IRL). The learned …

Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations

Learning agile skills is one of the main challenges in robotics. To this end, reinforcement learning approaches have achieved impressive results. These methods require explicit task information in terms of a reward function or an expert that can be queried in simulation to provide a target control output, which limits their applicability. In this work, we propose a generative adversarial method for inferring reward functions from partial and potentially physically incompatible demonstrations for successful skill acquirement where reference or expert demonstrations are not easily accessible. Mo…

Casper：利用视觉语言模型推断辅助遥操作中的多样化意图

Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models

Casper系统利用预训练视觉语言模型的常识知识，通过开放世界感知、VLM意图推断和技能库，在辅助遥操作中实时推断多样化人类意图并执行灵活技能，实验证明其优于直接遥操作和现有辅助遥操作基线。

Joint Estimation of Expertise and Reward Preferences From Human Demonstrations

When a robot learns from human examples, most approaches assume that the human partner provides examples of optimal behavior. However, there are applications in which the robot learns from non-expert humans. We argue that the robot should learn not only about the human's objectives, but also about their expertise level. The robot could then leverage this joint information to reduce or increase the frequency at which it provides assistance to its human's partner or be more cautious when learning new skills from novice users. Similarly, by taking into account the human's expertise, the robot wou…

通过空间基础合成世界实现机器人的具身认知

Towards Embodied Cognition in Robots via Spatially Grounded Synthetic Worlds

提出训练视觉语言模型执行视觉视角采择的概念框架，引入合成数据集用于空间推理任务监督学习，专注推断Z轴距离，数据集公开，为具身人工智能系统空间理解奠基。