48 papers with code • 0 benchmarks • 1 datasets
These leaderboards are used to track progress in Robot Manipulation
We have open-sourced our implementation to facilitate future research in learning to perform many complex manipulation skills in a row specified with natural language.
Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality.
Autonomous robot manipulation involves estimating the translation and orientation of the object to be manipulated as a 6-degree-of-freedom (6D) pose.
In order to exploit this idea, we introduce a framework whereby an object locomotion policy is initially obtained using a realistic physics simulator.
3D scene representation for robot manipulation should capture three key object properties: permanency -- objects that become occluded over time continue to exist; amodal completeness -- objects have 3D occupancy, even if only partial observations are available; spatiotemporal continuity -- the movement of each object is continuous over space and time.
We develop an end-to-end manipulation method based solely on detection and introduce Task-focused Few-shot Object Detection (TFOD) to learn new objects and settings.
This work shows that we can express a wide spectrum of robot manipulation tasks with multimodal prompts, interleaving textual and visual tokens.
Robot-world, hand-eye calibration is the problem of determining the transformation between the robot end-effector and a camera, as well as the transformation between the robot base and the world coordinate system.
End-to-end control for robot manipulation and grasping is emerging as an attractive alternative to traditional pipelined approaches.
In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction.