ALAN: Autonomously Exploring Robotic Agents in the Real World
- URL: http://arxiv.org/abs/2302.06604v1
- Date: Mon, 13 Feb 2023 18:59:09 GMT
- Title: ALAN: Autonomously Exploring Robotic Agents in the Real World
- Authors: Russell Mendonca, Shikhar Bahl, Deepak Pathak
- Abstract summary: ALAN is an autonomously exploring robotic agent that can perform tasks in the real world with little training and interaction time.
This is enabled by measuring environment change, which reflects object movement and ignores changes in the robot position.
We evaluate our approach on two different real-world play kitchen settings, enabling a robot to efficiently explore and discover manipulation skills.
- Score: 28.65531878636441
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Robotic agents that operate autonomously in the real world need to
continuously explore their environment and learn from the data collected, with
minimal human supervision. While it is possible to build agents that can learn
in such a manner without supervision, current methods struggle to scale to the
real world. Thus, we propose ALAN, an autonomously exploring robotic agent,
that can perform tasks in the real world with little training and interaction
time. This is enabled by measuring environment change, which reflects object
movement and ignores changes in the robot position. We use this metric directly
as an environment-centric signal, and also maximize the uncertainty of
predicted environment change, which provides agent-centric exploration signal.
We evaluate our approach on two different real-world play kitchen settings,
enabling a robot to efficiently explore and discover manipulation skills, and
perform tasks specified via goal images. Website at
https://robo-explorer.github.io/
Related papers
- Track2Act: Predicting Point Tracks from Internet Videos enables Diverse Zero-shot Robot Manipulation [65.46610405509338]
Track2Act predicts tracks of how points in an image should move in future time-steps based on a goal.
We use these 2D track predictions to infer a sequence of rigid transforms of the object to be manipulated, and obtain robot end-effector poses.
We show that this approach of combining scalably learned track prediction with a residual policy enables zero-shot robot manipulation.
arXiv Detail & Related papers (2024-05-02T17:56:55Z) - Growing from Exploration: A self-exploring framework for robots based on
foundation models [13.250831101705694]
We propose a framework named GExp, which enables robots to explore and learn autonomously without human intervention.
Inspired by the way that infants interact with the world, GExp encourages robots to understand and explore the environment with a series of self-generated tasks.
arXiv Detail & Related papers (2024-01-24T14:04:08Z) - FOCUS: Object-Centric World Models for Robotics Manipulation [4.6956495676681484]
FOCUS is a model-based agent that learns an object-centric world model.
We show that object-centric world models allow the agent to solve tasks more efficiently.
We also showcase how FOCUS could be adopted in real-world settings.
arXiv Detail & Related papers (2023-07-05T16:49:06Z) - HomeRobot: Open-Vocabulary Mobile Manipulation [107.05702777141178]
Open-Vocabulary Mobile Manipulation (OVMM) is the problem of picking any object in any unseen environment, and placing it in a commanded location.
HomeRobot has two components: a simulation component, which uses a large and diverse curated object set in new, high-quality multi-room home environments; and a real-world component, providing a software stack for the low-cost Hello Robot Stretch.
arXiv Detail & Related papers (2023-06-20T14:30:32Z) - Affordances from Human Videos as a Versatile Representation for Robotics [31.248842798600606]
We train a visual affordance model that estimates where and how in the scene a human is likely to interact.
The structure of these behavioral affordances directly enables the robot to perform many complex tasks.
We show the efficacy of our approach, which we call VRB, across 4 real world environments, over 10 different tasks, and 2 robotic platforms operating in the wild.
arXiv Detail & Related papers (2023-04-17T17:59:34Z) - Real-World Humanoid Locomotion with Reinforcement Learning [92.85934954371099]
We present a fully learning-based approach for real-world humanoid locomotion.
Our controller can walk over various outdoor terrains, is robust to external disturbances, and can adapt in context.
arXiv Detail & Related papers (2023-03-06T18:59:09Z) - Dual-Arm Adversarial Robot Learning [0.6091702876917281]
We propose dual-arm settings as platforms for robot learning.
We will discuss the potential benefits of this setup as well as the challenges and research directions that can be pursued.
arXiv Detail & Related papers (2021-10-15T12:51:57Z) - Rapid Exploration for Open-World Navigation with Latent Goal Models [78.45339342966196]
We describe a robotic learning system for autonomous exploration and navigation in diverse, open-world environments.
At the core of our method is a learned latent variable model of distances and actions, along with a non-parametric topological memory of images.
We use an information bottleneck to regularize the learned policy, giving us (i) a compact visual representation of goals, (ii) improved generalization capabilities, and (iii) a mechanism for sampling feasible goals for exploration.
arXiv Detail & Related papers (2021-04-12T23:14:41Z) - Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human
Videos [59.58105314783289]
Domain-agnostic Video Discriminator (DVD) learns multitask reward functions by training a discriminator to classify whether two videos are performing the same task.
DVD can generalize by virtue of learning from a small amount of robot data with a broad dataset of human videos.
DVD can be combined with visual model predictive control to solve robotic manipulation tasks on a real WidowX200 robot in an unseen environment from a single human demo.
arXiv Detail & Related papers (2021-03-31T05:25:05Z) - SAPIEN: A SimulAted Part-based Interactive ENvironment [77.4739790629284]
SAPIEN is a realistic and physics-rich simulated environment that hosts a large-scale set for articulated objects.
We evaluate state-of-the-art vision algorithms for part detection and motion attribute recognition as well as demonstrate robotic interaction tasks.
arXiv Detail & Related papers (2020-03-19T00:11:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.