Search Results for author:
Found 12 papers, 4 papers with code
Deep SE(3)-Equivariant Geometric Reasoning for Precise Placement Tasks
In this work, we propose a method for precise relative pose prediction which is provably SE(3)-equivariant, can be learned from only a few demonstrations, and can generalize across variations in a class of objects.
RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation
For robots to be useful outside labs and specialized factories we need a way to teach them new useful behaviors quickly.
TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement
We present a novel model for Tracking Any Point (TAP) that effectively tracks any queried point on any physical surface throughout a video sequence.
Ranked #1 on
Visual Tracking
on Kinetics
Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings
Dense object tracking, the ability to localize specific object points with pixel-level accuracy, is an important computer vision task with numerous downstream applications in robotics.
Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study
In this paper we define criteria for industry-oriented DRL, and perform a thorough comparison according to these criteria of one family of learning approaches, DRL from demonstration, against a professional industrial integrator on the recently established NIST assembly benchmark.
S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency
In this work we advocate semantic 3D keypoints as a visual representation, and present a semi-supervised training objective that can allow instance or category-level keypoints to be trained to 1-5 millimeter-accuracy with minimal supervision.
Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient
This work evaluates the use of model-based trajectory optimization methods used for exploration in Deep Deterministic Policy Gradient when trained on a latent image embedding.
Scaling data-driven robotics with reward sketching and batch reinforcement learning
We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions.
Generative predecessor models for sample-efficient imitation learning
We propose Generative Predecessor Models for Imitation Learning (GPRIL), a novel imitation learning algorithm that matches the state-action distribution to the distribution observed in expert demonstrations, using generative models to reason probabilistically about alternative histories of demonstrated states.
A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning
Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing.
Robotics
Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
We propose a general and model-free approach for Reinforcement Learning (RL) on real robotics with sparse rewards.
Sim-to-Real Robot Learning from Pixels with Progressive Nets
The progressive net approach is a general framework that enables reuse of everything from low-level visual features to high-level policies for transfer to new tasks, enabling a compositional, yet simple, approach to building complex skills.
