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Found 11 papers, 4 papers with code
M2T2: Multi-Task Masked Transformer for Object-centric Pick and Place
With the advent of large language models and large-scale robotic datasets, there has been tremendous progress in high-level decision-making for object manipulation.
CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation
CabiNet is a collision model that accepts object and scene point clouds, captured from a single-view depth observation, and predicts collisions for SE(3) object poses in the scene.
Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions
The pipeline of current robotic pick-and-place methods typically consists of several stages: grasp pose detection, finding inverse kinematic solutions for the detected poses, planning a collision-free trajectory, and then executing the open-loop trajectory to the grasp pose with a low-level tracking controller.
HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers
We analyze the performance of a set of baselines and show a correlation with a real-world evaluation.
Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented Grasping
This is largely due to the scale of the datasets both in terms of the number of objects and tasks studied.
6-DOF Grasping for Target-driven Object Manipulation in Clutter
Grasping in cluttered environments is a fundamental but challenging robotic skill.
PyRobot: An Open-source Robotics Framework for Research and Benchmarking
This paper introduces PyRobot, an open-source robotics framework for research and benchmarking.
Hardware Conditioned Policies for Multi-Robot Transfer Learning
In tasks where knowing the agent dynamics is important for success, we learn an embedding for robot hardware and show that policies conditioned on the encoding of hardware tend to generalize and transfer well.
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias
The models trained with our home dataset showed a marked improvement of 43. 7% over a baseline model trained with data collected in lab.
Learning to Grasp Without Seeing
We believe this is the first attempt at learning to grasp with only tactile sensing and without any prior object knowledge.
CASSL: Curriculum Accelerated Self-Supervised Learning
Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces.
