Search Results for author:
Found 15 papers, 7 papers with code
Beyond Games: Bringing Exploration to Robots in Real-world
But most importantly, we are able to implement an exploration policy on a robot which learns to interact with objects completely from scratch just using data collected via the differentiable exploration module.
droidlet: modular, heterogenous, multi-modal agents
In recent years, there have been significant advances in building end-to-end Machine Learning (ML) systems that learn at scale.
Visual Imitation Made Easy
We use commercially available reacher-grabber assistive tools both as a data collection device and as the robot's end-effector.
Swoosh! Rattle! Thump! -- Actions that Sound
In this work, we perform the first large-scale study of the interactions between sound and robotic action.
Object Goal Navigation using Goal-Oriented Semantic Exploration
We propose a modular system called, `Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently based on the goal object category.
Ranked #4 on
Robot Navigation
on Habitat 2020 Object Nav test-std
Learning to Explore using Active Neural SLAM
The use of learning provides flexibility with respect to input modalities (in the SLAM module), leverages structural regularities of the world (in global policies), and provides robustness to errors in state estimation (in local policies).
Object-centric Forward Modeling for Model Predictive Control
We present an approach to learn an object-centric forward model, and show that this allows us to plan for sequences of actions to achieve distant desired goals.
Swoosh! Rattle! Thump! - Actions that Sound
In this work, we perform the first large-scale study of the interactions between sound and robotic action.
PyRobot: An Open-source Robotics Framework for Research and Benchmarking
This paper introduces PyRobot, an open-source robotics framework for research and benchmarking.
Self-Supervised Exploration via Disagreement
In this paper, we propose a formulation for exploration inspired by the work in active learning literature.
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.
The Curious Robot: Learning Visual Representations via Physical Interactions
We argue that biological agents use physical interactions with the world to learn visual representations unlike current vision systems which just use passive observations (images and videos downloaded from web).
