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Found 14 papers, 5 papers with code
Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations
Although policies learned with RC sampling can be superior to HC sampling for standard learning models such as linear SVMs, policies learned with HC sampling may be comparable with highly-expressive learning models such as deep learning and hyper-parametric decision trees, which have little model error.
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
To reduce data collection time for deep learning of robust robotic grasp plans, we explore training from a synthetic dataset of 6. 7 million point clouds, grasps, and analytic grasp metrics generated from thousands of 3D models from Dex-Net 1. 0 in randomized poses on a table.
Robotics
DART: Noise Injection for Robust Imitation Learning
One approach to Imitation Learning is Behavior Cloning, in which a robot observes a supervisor and infers a control policy.
Learning Robust Bed Making using Deep Imitation Learning with DART
Bed-making is a universal home task that can be challenging for senior citizens due to reaching motions.
Robotics
Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making
We compare coverage results from (1) human supervision, (2) a baseline of picking at the uppermost blanket point, and (3) learned pick points.
Dynamic Regret Convergence Analysis and an Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning
In this article, we reframe this result using dynamic regret theory from the field of online optimization and show that dynamic regret can be applied to any on-policy algorithm to analyze its convergence and optimality.
A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes
We describe a mobile manipulation hardware and software system capable of autonomously performing complex human-level tasks in real homes, after being taught the task with a single demonstration from a person in virtual reality.
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data
We address these challenges using interpretable deep visual representations for rope, extending recent work on dense object descriptors for robot manipulation.
Untangling Dense Knots by Learning Task-Relevant Keypoints
HULK successfully untangles a cable from a dense initial configuration containing up to two overhand and figure-eight knots in 97. 9% of 378 simulation experiments with an average of 12. 1 actions per trial.
Disentangling Dense Multi-Cable Knots
Disentangling two or more cables requires many steps to remove crossings between and within cables.
Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies
We present two algorithms that enhance robust cable untangling, LOKI and SPiDERMan, which operate alongside HULK, a high-level planner from prior work.
SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo
However, the RGB-D baseline only grasps 35% of the hard (e. g., transparent) objects, while SimNet grasps 95%, suggesting that SimNet can enable robust manipulation of unknown objects, including transparent objects, in unknown environments.
CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation
This paper studies the complex task of simultaneous multi-object 3D reconstruction, 6D pose and size estimation from a single-view RGB-D observation.
Ranked #1 on
6D Pose Estimation using RGBD
on CAMERA25
Autonomously Untangling Long Cables
Cables are ubiquitous in many settings and it is often useful to untangle them.
