Search Results for author:
Found 17 papers, 3 papers with code
Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation
Our neural representation driven by multimodal sensing can serve as a perception backbone towards advancing robot dexterity.
Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots
We present Habitat 3. 0: a simulation platform for studying collaborative human-robot tasks in home environments.
What do we learn from a large-scale study of pre-trained visual representations in sim and real environments?
We present a large empirical investigation on the use of pre-trained visual representations (PVRs) for training downstream policies that execute real-world tasks.
USA-Net: Unified Semantic and Affordance Representations for Robot Memory
In order for robots to follow open-ended instructions like "go open the brown cabinet over the sink", they require an understanding of both the scene geometry and the semantics of their environment.
How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned
Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains.
Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data
Similar to computer vision problems, such as object detection, Action Image builds on the idea that object features are invariant to translation in image space.
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
Imitation learning allows agents to learn complex behaviors from demonstrations.
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping
The absence of an actor in Q2-Opt allows us to directly draw a parallel to the previous discrete experiments in the literature without the additional complexities induced by an actor-critic architecture.
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
Imitation learning allows agents to learn complex behaviors from demonstrations.
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping
Our actor model reduces the inference time by 3 times compared to the state-of-the-art CEM method.
Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks
Using domain adaptation methods to cross this "reality gap" requires a large amount of unlabelled real-world data, whilst domain randomization alone can waste modeling power.
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach.
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
Learning-based approaches to robotic manipulation are limited by the scalability of data collection and accessibility of labels.
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
We extensively evaluate our approaches with a total of more than 25, 000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN.
Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search
In this work, we explore distributed and asynchronous policy learning as a means to achieve generalization and improved training times on challenging, real-world manipulation tasks.
Path Integral Guided Policy Search
We extend GPS in the following ways: (1) we propose the use of a model-free local optimizer based on path integral stochastic optimal control (PI2), which enables us to learn local policies for tasks with highly discontinuous contact dynamics; and (2) we enable GPS to train on a new set of task instances in every iteration by using on-policy sampling: this increases the diversity of the instances that the policy is trained on, and is crucial for achieving good generalization.
Bayesian Kernel Shaping for Learning Control
In this paper, we focus on nonparametric regression and introduce a Bayesian formulation that, with the help of variational approximations, results in an EM-like algorithm for simultaneous estimation of regression and kernel parameters.
