VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought
Large-scale generative language and vision-language models (LLMs and VLMs) excel in
few-shot in-context learning for decision making and instruction following. However, they …
few-shot in-context learning for decision making and instruction following. However, they …
DABI: Evaluation of Data Augmentation Methods Using Downsampling in Bilateral Control-Based Imitation Learning with Images
M Kobayashi, T Buamanee, Y Uranishi - arXiv preprint arXiv:2410.04370, 2024 - arxiv.org
Autonomous robot manipulation is a complex and continuously evolving robotics field. This
paper focuses on data augmentation methods in imitation learning. Imitation learning …
paper focuses on data augmentation methods in imitation learning. Imitation learning …
VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs
Large-scale generative language and vision-language models excel in in-context learning
for decision making. However, they require high-quality exemplar demonstrations to be …
for decision making. However, they require high-quality exemplar demonstrations to be …
Language-guided Robust Navigation for Mobile Robots in Dynamically-changing Environments
In this paper, we develop an embodied AI system for human-in-the-loop navigation with a
wheeled mobile robot. We propose a direct yet effective method of monitoring the robot's …
wheeled mobile robot. We propose a direct yet effective method of monitoring the robot's …
RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning
Developing robust and correctable visuomotor policies for robotic manipulation is
challenging due to the lack of self-recovery mechanisms from failures and the limitations of …
challenging due to the lack of self-recovery mechanisms from failures and the limitations of …
Incremental Learning for Robot Shared Autonomy
Y Tao, G Qiao, D Ding, Z Erickson - arXiv preprint arXiv:2410.06315, 2024 - arxiv.org
Shared autonomy holds promise for improving the usability and accessibility of assistive
robotic arms, but current methods often rely on costly expert demonstrations and lack the …
robotic arms, but current methods often rely on costly expert demonstrations and lack the …
Task Success Prediction and Open-Vocabulary Object Manipulation
This study addresses a task designed to predict the future success or failure of open-
vocabulary object manipulation. In this task, the model is required to make predictions based …
vocabulary object manipulation. In this task, the model is required to make predictions based …
Diff-DAgger: Uncertainty Estimation with Diffusion Policy for Robotic Manipulation
SW Lee, YL Kuo - arXiv preprint arXiv:2410.14868, 2024 - arxiv.org
Recently, diffusion policy has shown impressive results in handling multi-modal tasks in
robotic manipulation. However, it has fundamental limitations in out-of-distribution failures …
robotic manipulation. However, it has fundamental limitations in out-of-distribution failures …
Scalable Lifelong Imitation Learning for Robot Fleets
R Hoque - 2024 - search.proquest.com
Recent breakthroughs in deep learning have revolutionized natural language processing,
computer vision, and robotics. Nevertheless, reliable robot autonomy in unstructured …
computer vision, and robotics. Nevertheless, reliable robot autonomy in unstructured …
Multi-Strategy Deployment-Time Learning and Adaptation for Navigation under Uncertainty
We present an approach for performant point-goal navigation in unfamiliar partially-mapped
environments. When deployed, our robot runs multiple strategies for deployment-time …
environments. When deployed, our robot runs multiple strategies for deployment-time …