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Found 14 papers, 4 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.
LEMMo-Plan: LLM-Enhanced Learning from Multi-Modal Demonstration for Planning Sequential Contact-Rich Manipulation Tasks
To enhance the validity of LLM-generated plans, visual demonstrations and online videos have been widely employed to guide the planning process.
Anthropomorphic Grasping with Neural Object Shape Completion
The progressive prevalence of robots in human-suited environments has given rise to a myriad of object manipulation techniques, in which dexterity plays a paramount role.
The Next Evolution of Artificial Sense of Touch
We propose the next evolution of the artificial sense of touch, including an in-depth examination of the latest advancements in tactile sensing technology and the challenges that remain.
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.
Care3D: An Active 3D Object Detection Dataset of Real Robotic-Care Environments
As labor shortage increases in the health sector, the demand for assistive robotics grows.
Optimally Controlling the Timing of Energy Transfer in Elastic Joints: Experimental Validation of the Bi-Stiffness Actuation Concept
Furthermore, we demonstrate that providing full control authority over the energy transfer timing and link decoupling allows the user to synchronously release both elastic joint and gravitational energy.
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.
Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space
We combine these components through a bio-inspired action space that we call AFORCE.
SyReNets: Symbolic Residual Neural Networks
In this paper, we propose SyReNets, an approach that leverages neural networks for learning symbolic relations to accurately describe dynamic physical systems from data.
Learning Vision-based Reactive Policies for Obstacle Avoidance
In this paper, we address the problem of vision-based obstacle avoidance for robotic manipulators.
How to Make Deep RL Work in Practice
Additionally, techniques from supervised learning are often used by default but influence the algorithms in a reinforcement learning setting in different and not well-understood ways.
Tracking Holistic Object Representations
The framework leverages the idea of obtaining additional object templates during the tracking process.
Ranked #3 on
Visual Object Tracking
on VOT2017/18
