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Found 6 papers, 1 papers with code
Geometrically-Aware One-Shot Skill Transfer of Category-Level Objects
Robotic manipulation of unfamiliar objects in new environments is challenging and requires extensive training or laborious pre-programming.
DRAPER: Towards a Robust Robot Deployment and Reliable Evaluation for Quasi-Static Pick-and-Place Cloth-Shaping Neural Controllers
The fidelity gap between simulation-trained cloth neural controllers and real-world operation hinders the reliable deployment of these methods in physical trials.
Using The Concept Hierarchy for Household Action Recognition
We propose a method to systematically represent both the static and the dynamic components of environments, i. e. objects and agents, as well as the changes that are happening in the environment, i. e. the actions and skills performed by agents.
LoHoRavens: A Long-Horizon Language-Conditioned Benchmark for Robotic Tabletop Manipulation
The convergence of embodied agents and large language models (LLMs) has brought significant advancements to embodied instruction following.
LATTE: LAnguage Trajectory TransformEr
Natural language is one of the most intuitive ways to express human intent.
Reshaping Robot Trajectories Using Natural Language Commands: A Study of Multi-Modal Data Alignment Using Transformers
However, using language is seldom an easy task when humans need to express their intent towards robots, since most of the current language interfaces require rigid templates with a static set of action targets and commands.
