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Found 25 papers, 3 papers with code
A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty
Accurate rotation estimation is at the heart of robot perception tasks such as visual odometry and object pose estimation.
AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers
Rather than using LLMs to directly plan task sub-goals, we instead perform few-shot translation from natural language task descriptions to an intermediate task representation that can then be consumed by a TAMP algorithm to jointly solve the task and motion plan.
PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Preference Alignment
Prompt optimization aims to find the best prompt to a large language model (LLM) for a given task.
Admissible Abstractions for Near-optimal Task and Motion Planning
We define an admissibility condition for abstractions expressed using angelic semantics and show that these conditions allow us to accelerate planning while preserving the ability to find the optimal motion plan.
Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models
The gap between our ability to collect interesting data and our ability to analyze these data is growing at an unprecedented rate.
Generalized Grounding Graphs: A Probabilistic Framework for Understanding Grounded Commands
Symbolic models capture linguistic structure but have not scaled successfully to handle the diverse language produced by untrained users.
PROBE-GK: Predictive Robust Estimation using Generalized Kernels
Many algorithms in computer vision and robotics make strong assumptions about uncertainty, and rely on the validity of these assumptions to produce accurate and consistent state estimates.
Structural Return Maximization for Reinforcement Learning
Batch Reinforcement Learning (RL) algorithms attempt to choose a policy from a designer-provided class of policies given a fixed set of training data.
Modelling Observation Correlations for Active Exploration and Robust Object Detection
In particular, the performance of detection algorithms is commonly sensitive to the position of the sensor relative to the objects in the scene.
Efficient Planning under Uncertainty with Macro-actions
We also demonstrate our algorithm being used to control a real robotic helicopter in a target monitoring experiment, which suggests that our approach has practical potential for planning in real-world, large partially observable domains where a multi-step lookahead is required to achieve good performance.
Batch-iFDD for Representation Expansion in Large MDPs
Matching pursuit (MP) methods are a promising class of feature construction algorithms for value function approximation.
Nonparametric Bayesian Policy Priors for Reinforcement Learning
We consider reinforcement learning in partially observable domains where the agent can query an expert for demonstrations.
FLaME: Fast Lightweight Mesh Estimation Using Variational Smoothing on Delaunay Graphs
We propose a lightweight method for dense online monocular depth estimation capable of reconstructing 3D meshes on computationally constrained platforms.
Leveraging Past References for Robust Language Grounding
We therefore reframe the grounding problem from the perspective of coreference detection and propose a neural network that detects when two expressions are referring to the same object.
Visual Prediction of Priors for Articulated Object Interaction
Given a novel object, the objective is to maximize reward with few interactions.
Online Descriptor Enhancement via Self-Labelling Triplets for Visual Data Association
We show that self-labelling challenging triplets--choosing positive examples separated by large temporal distances and negative examples close in the descriptor space--improves the quality of the learned descriptors for the multi-object tracking task.
Language Understanding for Field and Service Robots in a Priori Unknown Environments
This progress now creates an opportunity for robots to operate not only in isolation, but also with and alongside humans in our complex environments.
Active Learning of Abstract Plan Feasibility
Long horizon sequential manipulation tasks are effectively addressed hierarchically: at a high level of abstraction the planner searches over abstract action sequences, and when a plan is found, lower level motion plans are generated.
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains.
Structured Latent Variable Models for Articulated Object Interaction
In this paper, we investigate a scenario in which a robot learns a low-dimensional representation of a door given a video of the door opening or closing.
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy
For uncertainty quantification, we efficiently model both aleatoric and epistemic uncertainty by learning discrete traction distributions and probability densities of the traction predictor's latent features.
Scaling Is All You Need: Autonomous Driving with JAX-Accelerated Reinforcement Learning
However, running reinforcement learning experiments on the required scale for autonomous driving is extremely difficult.
Large Language Models to the Rescue: Deadlock Resolution in Multi-Robot Systems
Multi-agent robotic systems are prone to deadlocks in an obstacle environment where the system can get stuck away from its desired location under a smooth low-level control policy.
