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Found 13 papers, 2 papers with code
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment.
Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
Large pretrained (e. g., "foundation") models exhibit distinct capabilities depending on the domain of data they are trained on.
Ranked #8 on
Video Retrieval
on MSR-VTT-1kA
(video-to-text R@1 metric)
Chain of Thought Prompting Elicits Reasoning in Large Language Models
We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning.
Mechanical Search on Shelves using a Novel "Bluction" Tool
Shelves are common in homes, warehouses, and commercial settings due to their storage efficiency.
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning
Hierarchical reinforcement learning aims to enable this by providing a bank of low-level skills as action abstractions.
Multi-Task Learning with Sequence-Conditioned Transporter Networks
Enabling robots to solve multiple manipulation tasks has a wide range of industrial applications.
Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation
However, goal images also have a number of drawbacks: they are inconvenient for humans to provide, they can over-specify the desired behavior leading to a sparse reward signal, or under-specify task information in the case of non-goal reaching tasks.
Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning
This task space can be quite general and abstract; its only requirements are to be sampleable and to well-cover the space of useful tasks.
Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous
We propose hierarchical predictive planning (HPP), a model-based reinforcement learning method for decentralized multiagent rendezvous.
Neural Collision Clearance Estimator for Batched Motion Planning
We present a neural network collision checking heuristic, ClearanceNet, and a planning algorithm, CN-RRT.
Learned Critical Probabilistic Roadmaps for Robotic Motion Planning
Critical PRMs are demonstrated to achieve up to three orders of magnitude improvement over uniform sampling, while preserving the guarantees and complexity of sampling-based motion planning.
Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation
Here, we propose a zero-shot imitation learning approach for training a visual navigation policy on legged robots from human (third-person perspective) demonstrations, enabling high-quality navigation and cost-effective data collection.
Learning Sampling Distributions for Robot Motion Planning
This paper proposes a methodology for non-uniform sampling, whereby a sampling distribution is learned from demonstrations, and then used to bias sampling.
