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Found 33 papers, 5 papers with code
Controller Design for Bilinear Neural Feedback Loops
This paper considers a class of bilinear systems with a neural network in the loop.
Guiding Data Collection via Factored Scaling Curves
Generalist imitation learning policies trained on large datasets show great promise for solving diverse manipulation tasks.
Learning to Drive Anywhere with Model-Based Reannotation
Developing broadly generalizable visual navigation policies for robots is a significant challenge, primarily constrained by the availability of large-scale, diverse training data.
ResearchCodeAgent: An LLM Multi-Agent System for Automated Codification of Research Methodologies
In this paper we introduce ResearchCodeAgent, a novel multi-agent system leveraging large language models (LLMs) agents to automate the codification of research methodologies described in machine learning literature.
A Taxonomy for Evaluating Generalist Robot Policies
In this work, our goal is (1) to outline the forms of generalization we believe are important in robot manipulation in a comprehensive and fine-grained manner, and (2) to provide reproducible guidelines for measuring these notions of generalization.
Vision Language Models are In-Context Value Learners
Instead, GVL poses value estimation as a temporal ordering problem over shuffled video frames; this seemingly more challenging task encourages VLMs to more fully exploit their underlying semantic and temporal grounding capabilities to differentiate frames based on their perceived task progress, consequently producing significantly better value predictions.
STEER: Flexible Robotic Manipulation via Dense Language Grounding
The complexity of the real world demands robotic systems that can intelligently adapt to unseen situations.
Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation
To train the policy, we use an order of magnitude less robot interaction data compared to what the video prediction model was trained on.
A System and Benchmark for LLM-based Q&A on Heterogeneous Data
To demonstrate the effectiveness of siwarex, we extend the popular Spider dataset and benchmark by replacing some of its tables by data retrieval APIs.
Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs
The high-level policy consists of a long-context VLM that takes the demonstration tour video and the multimodal user instruction as input to find the goal frame in the tour video.
SELFI: Autonomous Self-Improvement with Reinforcement Learning for Social Navigation
Specifically, SELFI stabilizes the online learning process by incorporating the same model-based learning objective from offline pre-training into the Q-values learned with online model-free reinforcement learning.
An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories
We introduce a new class of data-driven and distribution-free optimistic-robust bimodal inventory optimization (BIO) strategy to effectively allocate inventory across a retail chain to meet time-varying, uncertain omnichannel demand.
Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning
Navigation in unfamiliar environments presents a major challenge for robots: while mapping and planning techniques can be used to build up a representation of the world, quickly discovering a path to a desired goal in unfamiliar settings with such methods often requires lengthy mapping and exploration.
NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration
In this paper, we describe how we can train a single unified diffusion policy to handle both goal-directed navigation and goal-agnostic exploration, with the latter providing the ability to search novel environments, and the former providing the ability to reach a user-specified goal once it has been located.
ViNT: A Foundation Model for Visual Navigation
In this paper, we describe the Visual Navigation Transformer (ViNT), a foundation model that aims to bring the success of general-purpose pre-trained models to vision-based robotic navigation.
SACSoN: Scalable Autonomous Control for Social Navigation
By minimizing this counterfactual perturbation, we can induce robots to behave in ways that do not alter the natural behavior of humans in the shared space.
Machine Vision Using Cellphone Camera: A Comparison of deep networks for classifying three challenging denominations of Indian Coins
Therefore, it was hypothesized that a digital image of a coin resting on its either size could be classified into its correct denomination by training a deep neural network model.
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing
We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL).
Grounded Decoding: Guiding Text Generation with Grounded Models for Embodied Agents
Recent progress in large language models (LLMs) has demonstrated the ability to learn and leverage Internet-scale knowledge through pre-training with autoregressive models.
Offline Reinforcement Learning for Visual Navigation
Reinforcement learning can enable robots to navigate to distant goals while optimizing user-specified reward functions, including preferences for following lanes, staying on paved paths, or avoiding freshly mowed grass.
Learning Robotic Navigation from Experience: Principles, Methods, and Recent Results
Navigation is one of the most heavily studied problems in robotics, and is conventionally approached as a geometric mapping and planning problem.
ExAug: Robot-Conditioned Navigation Policies via Geometric Experience Augmentation
Machine learning techniques rely on large and diverse datasets for generalization.
GNM: A General Navigation Model to Drive Any Robot
Learning provides a powerful tool for vision-based navigation, but the capabilities of learning-based policies are constrained by limited training data.
LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action
Goal-conditioned policies for robotic navigation can be trained on large, unannotated datasets, providing for good generalization to real-world settings.
ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints
In this work, we propose an approach that integrates learning and planning, and can utilize side information such as schematic roadmaps, satellite maps and GPS coordinates as a planning heuristic, without relying on them being accurate.
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.
Hierarchical Reinforcement Learning
reinforcement-learning
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Rapid Exploration for Open-World Navigation with Latent Goal Models
We describe a robotic learning system for autonomous exploration and navigation in diverse, open-world environments.
ViNG: Learning Open-World Navigation with Visual Goals
We propose a learning-based navigation system for reaching visually indicated goals and demonstrate this system on a real mobile robot platform.
Effect Of Weather Conditions On FSO Link
Free Space Optics (FSO) is a developing technology for Line of Sight communication that uses light propagation in free space that provides various advantages like high bandwidth, high data rate, ease of installation, free licensing and secure communication.
The Ingredients of Real World Robotic Reinforcement Learning
The success of reinforcement learning in the real world has been limited to instrumented laboratory scenarios, often requiring arduous human supervision to enable continuous learning.
The Ingredients of Real-World Robotic Reinforcement Learning
In this work, we discuss the elements that are needed for a robotic learning system that can continually and autonomously improve with data collected in the real world.
Machine learning based co-creative design framework
We propose a flexible, co-creative framework bringing together multiple machine learning techniques to assist human users to efficiently produce effective creative designs.
Toward A Neuro-inspired Creative Decoder
Creativity, a process that generates novel and meaningful ideas, involves increased association between task-positive (control) and task-negative (default) networks in the human brain.
