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Found 31 papers, 11 papers with code
LuciBot: Automated Robot Policy Learning from Generated Videos
While prior works use large language models (LLMs) or vision-language models (VLMs) to generate rewards, these approaches are largely limited to simple tasks with well-defined rewards, such as pick-and-place.
Articulate AnyMesh: Open-Vocabulary 3D Articulated Objects Modeling
3D articulated objects modeling has long been a challenging problem, since it requires to capture both accurate surface geometries and semantically meaningful and spatially precise structures, parts, and joints.
Embodied Red Teaming for Auditing Robotic Foundation Models
Language-conditioned robot models have the potential to enable robots to perform a wide range of tasks based on natural language instructions.
Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting
Creating large-scale interactive 3D environments is essential for the development of Robotics and Embodied AI research.
Flex: End-to-End Text-Instructed Visual Navigation from Foundation Model Features
End-to-end learning directly maps sensory inputs to actions, creating highly integrated and efficient policies for complex robotics tasks.
Human Insights Driven Latent Space for Different Driving Perspectives: A Unified Encoder for Efficient Multi-Task Inference
While our performance in steering angle estimation is comparable to existing methods, the integration of human-like perception through multi-task learning holds significant potential for advancing autonomous driving systems.
ABNet: Attention BarrierNet for Safe and Scalable Robot Learning
Safe learning is central to AI-enabled robots where a single failure may lead to catastrophic results.
LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery
Large Language Models have recently gained significant attention in scientific discovery for their extensive knowledge and advanced reasoning capabilities.
Probing Multimodal LLMs as World Models for Driving
We provide a sober look at the application of Multimodal Large Language Models (MLLMs) in autonomous driving, challenging common assumptions about their ability to interpret dynamic driving scenarios.
Toward Efficient Visual Gyroscopes: Spherical Moments, Harmonics Filtering, and Masking Techniques for Spherical Camera Applications
Unlike a traditional gyroscope, a visual gyroscope estimates camera rotation through images.
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations
Grounding the common-sense reasoning of Large Language Models (LLMs) in physical domains remains a pivotal yet unsolved problem for embodied AI.
Curiosity-driven Red-teaming for Large Language Models
To probe when an LLM generates unwanted content, the current paradigm is to recruit a \textit{red team} of human testers to design input prompts (i. e., test cases) that elicit undesirable responses from LLMs.
RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
We present RoboGen, a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation.
Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models
As autonomous driving technology matures, end-to-end methodologies have emerged as a leading strategy, promising seamless integration from perception to control via deep learning.
SafeDiffuser: Safe Planning with Diffusion Probabilistic Models
Diffusion model-based approaches have shown promise in data-driven planning, but there are no safety guarantees, thus making it hard to be applied for safety-critical applications.
Towards Generalist Robots: A Promising Paradigm via Generative Simulation
This document serves as a position paper that outlines the authors' vision for a potential pathway towards generalist robots.
Learning Stability Attention in Vision-based End-to-end Driving Policies
Modern end-to-end learning systems can learn to explicitly infer control from perception.
SoftZoo: A Soft Robot Co-design Benchmark For Locomotion In Diverse Environments
Existing work has typically been tailored for particular environments or representations.
Towards Cooperative Flight Control Using Visual-Attention
We propose an air-guardian system that facilitates cooperation between a pilot with eye tracking and a parallel end-to-end neural control system.
Interpreting Neural Policies with Disentangled Tree Representations
The advancement of robots, particularly those functioning in complex human-centric environments, relies on control solutions that are driven by machine learning.
On the Forward Invariance of Neural ODEs
We propose a new method to ensure neural ordinary differential equations (ODEs) satisfy output specifications by using invariance set propagation.
Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap
Our results imply that the causality gap can be solved in situation one with our proposed training guideline with any modern network architecture, whereas achieving out-of-distribution generalization (situation two) requires further investigations, for instance, on data diversity rather than the model architecture.
Liquid Structural State-Space Models
A proper parametrization of state transition matrices of linear state-space models (SSMs) followed by standard nonlinearities enables them to efficiently learn representations from sequential data, establishing the state-of-the-art on a large series of long-range sequence modeling benchmarks.
Ranked #1 on
SpO2 estimation
on BIDMC
Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving
They are interpretable at scale, achieve great test performance under limited training data, and are safety guaranteed in a series of autonomous driving scenarios such as lane keeping and obstacle avoidance.
Learning Interactive Driving Policies via Data-driven Simulation
Data-driven simulators promise high data-efficiency for driving policy learning.
VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles
Simulation has the potential to transform the development of robust algorithms for mobile agents deployed in safety-critical scenarios.
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles.
Ranked #1 on
3D Object Detection
on OPV2V
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization
The complementary characteristics of active and passive depth sensing techniques motivate the fusion of the Li-DAR sensor and stereo camera for improved depth perception.
Point-to-Point Video Generation
We introduce point-to-point video generation that controls the generation process with two control points: the targeted start- and end-frames.
Plug-and-Play: Improve Depth Estimation via Sparse Data Propagation
We propose a novel plug-and-play (PnP) module for improving depth prediction with taking arbitrary patterns of sparse depths as input.
Omnidirectional CNN for Visual Place Recognition and Navigation
Given a visual input, the task of the O-CNN is not to retrieve the matched place exemplar, but to retrieve the closest place exemplar and estimate the relative distance between the input and the closest place.
