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Found 19 papers, 13 papers with code
MatMamba: A Matryoshka State Space Model
In this work, we present MatMamba: a state space model which combines Matryoshka-style learning with Mamba2, by modifying the block to contain nested dimensions to enable joint training and adaptive inference.
Logically Constrained Robotics Transformers for Enhanced Perception-Action Planning
With the advent of large foundation model based planning, there is a dire need to ensure their output aligns with the stakeholder's intent.
EvDNeRF: Reconstructing Event Data with Dynamic Neural Radiance Fields
We present EvDNeRF, a pipeline for generating event data and training an event-based dynamic NeRF, for the purpose of faithfully reconstructing eventstreams on scenes with rigid and non-rigid deformations that may be too fast to capture with a standard camera.
GRID: A Platform for General Robot Intelligence Development
In addition, the modular design enables various deep ML components and existing foundation models to be easily usable in a wider variety of robot-centric problems.
Is Imitation All You Need? Generalized Decision-Making with Dual-Phase Training
We introduce DualMind, a generalist agent designed to tackle various decision-making tasks that addresses challenges posed by current methods, such as overfitting behaviors and dependence on task-specific fine-tuning.
ConBaT: Control Barrier Transformer for Safe Policy Learning
In this work, we propose Control Barrier Transformer (ConBaT), an approach that learns safe behaviors from demonstrations in a self-supervised fashion.
ChatGPT for Robotics: Design Principles and Model Abilities
This paper presents an experimental study regarding the use of OpenAI's ChatGPT for robotics applications.
Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022
In this report, we present our approach and empirical results of applying masked autoencoders in two egocentric video understanding tasks, namely, Object State Change Classification and PNR Temporal Localization, of Ego4D Challenge 2022.
Learning Modular Simulations for Homogeneous Systems
We evaluate our framework on a variety of systems and show that message passing allows coordination between multiple modules over time for accurate predictions and in certain cases, enables zero-shot generalization to new system configurations.
Learning to Simulate Realistic LiDARs
Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling.
PACT: Perception-Action Causal Transformer for Autoregressive Robotics Pre-Training
Robotics has long been a field riddled with complex systems architectures whose modules and connections, whether traditional or learning-based, require significant human expertise and prior knowledge.
LATTE: LAnguage Trajectory TransformEr
Natural language is one of the most intuitive ways to express human intent.
Missingness Bias in Model Debugging
Missingness, or the absence of features from an input, is a concept fundamental to many model debugging tools.
CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning
The ability to perform causal and counterfactual reasoning are central properties of human intelligence.
3DB: A Framework for Debugging Computer Vision Models
We introduce 3DB: an extendable, unified framework for testing and debugging vision models using photorealistic simulation.
Representation Learning for Event-based Visuomotor Policies
Event-based cameras are dynamic vision sensors that provide asynchronous measurements of changes in per-pixel brightness at a microsecond level.
Unadversarial Examples: Designing Objects for Robust Vision
We study a class of realistic computer vision settings wherein one can influence the design of the objects being recognized.
AirSim Drone Racing Lab
Autonomous drone racing is a challenging research problem at the intersection of computer vision, planning, state estimation, and control.
Monocular Vision based Collaborative Localization for Micro Aerial Vehicle Swarms
This collaborative localization approach is built upon a distributed algorithm where individual and relative pose estimation techniques are combined for the group to localize against surrounding environments.
Robotics
