7 citations · 8 across the 16 of their papers we have counts for
18 papers
Training-free Behavior Cloning
Maximilian Adang, Timothy Chen, Lars Osterberg +2
Neural behavior cloning compresses demonstrations into large models, making individual actions difficult to trace and policy updates costly. Retrieval policies retain access to dem…
InSight: Self-Guided Skill Acquisition via Steerable VLAs
Maggie Wang, Lars Osterberg, Stephen Tian +5
Vision-language-action (VLA) models excel at robot manipulation via imitation learning, but adapting them to new tasks often requires additional human demonstrations, which can be…
Transformer-Based Warm-Starting for Feasible and Optimal Terminal Approach to Tumbling Objects with Space Manipulators
Yuji Takubo, Maximilian Adang, Mac Schwager +1
Autonomous capture of a tumbling object with a space manipulator requires trajectory generation under real-time onboard constraints. However, coupled bus-arm dynamics, rotating saf…
Robots Need More than VLA and World Models
Elis Karcini, Faisal Mehrban, Quang Nguyen +6
Generalist robot intelligence is often framed as a policy-scaling problem: collect more robot demonstrations, train larger Vision-Language-Action (VLA) models, and expect broader g…
Coverage Optimization for Camera View Selection
Timothy Chen, Adam Dai, Maximilian Adang +2
What makes a good viewpoint? The quality of the data used to learn 3D reconstructions is crucial for enabling efficient and accurate scene modeling. We study the active view select…
, But Make It Fly: Physics-Guided Transfer of VLA Models to Aerial Manipulation
Johnathan Tucker, Denis Liu, Aiden Swann +7
Vision-Language-Action (VLA) models such as have demonstrated remarkable generalization across diverse fixed-base manipulators. However, transferring these foundation models…
Foundational World Models Accurately Detect Bimanual Manipulator Failures
Isaac R. Ward, Michelle Ho, Houjun Liu +7
Deploying visuomotor robots at scale is challenging due to the potential for anomalous failures to degrade performance, cause damage, or endanger human life. Bimanual manipulators…
Sparse Autoencoders Reveal Interpretable and Steerable Features in VLA Models
Aiden Swann, Lachlain McGranahan, Hugo Buurmeijer +2
Vision-Language-Action (VLA) models have emerged as a promising approach for general-purpose robot manipulation. However, little research has mechanistically explored when and why…
Semantic-Metric Bayesian Risk Fields: Learning Robot Safety from Human Videos with a VLM Prior
Timothy Chen, Marcus Dominguez-Kuhne, Aiden Swann +2
Humans interpret safety not as a binary signal but as a continuous, context- and spatially-dependent notion of risk. While risk is subjective, humans form rational mental models th…
Conformal Safety Monitoring for Flight Testing: A Case Study in Data-Driven Safety Learning
Aaron O. Feldman, D. Isaiah Harp, Joseph Duncan +1
We develop a data-driven approach for runtime safety monitoring in flight testing, where pilots perform maneuvers on aircraft with uncertain parameters. Because safety violations c…
SketchPlan: Diffusion Based Drone Planning From Human Sketches
Sixten Norelius, Aaron O. Feldman, Mac Schwager
We propose SketchPlan, a diffusion-based planner that interprets 2D hand-drawn sketches over depth images to generate 3D flight paths for drone navigation. SketchPlan comprises two…
SINGER: An Onboard Generalist Vision-Language Navigation Policy for Drones
Maximilian Adang, JunEn Low, Ola Shorinwa +1
Large vision-language models have driven remarkable progress in open-vocabulary robot policies, e.g., generalist robot manipulation policies, that enable robots to complete complex…
VISTA: Open-Vocabulary, Task-Relevant Robot Exploration with Online Semantic Gaussian Splatting
Keiko Nagami, Timothy Chen, Javier Yu +5
We present VISTA (Viewpoint-based Image selection with Semantic Task Awareness), an active exploration method for robots to plan informative trajectories that improve 3D map qualit…
GRaD-Nav++: Vision-Language Model Enabled Visual Drone Navigation with Gaussian Radiance Fields and Differentiable Dynamics
Qianzhong Chen, Naixiang Gao, Suning Huang +4
Autonomous drones capable of interpreting and executing high-level language instructions in unstructured environments remain a long-standing goal. Yet existing approaches are const…
GRaD-Nav: Efficiently Learning Visual Drone Navigation with Gaussian Radiance Fields and Differentiable Dynamics
Qianzhong Chen, Jiankai Sun, Naixiang Gao +3
Autonomous visual navigation is an essential element in robot autonomy. Reinforcement learning (RL) offers a promising policy training paradigm. However existing RL methods suffer…
HAMMER: Heterogeneous, Multi-Robot Semantic Gaussian Splatting
Javier Yu, Timothy Chen, Mac Schwager
3D Gaussian Splatting offers expressive scene reconstruction, modeling a broad range of visual, geometric, and semantic information. However, efficient real-time map reconstruction…
Learning Robot Safety from Sparse Human Feedback using Conformal Prediction
Aaron O. Feldman, Joseph A. Vincent, Maximilian Adang +2
Ensuring robot safety can be challenging; user-defined constraints can miss edge cases, policies can become unsafe even when trained from safe data, and safety can be subjective. T…
SOUS VIDE: Cooking Visual Drone Navigation Policies in a Gaussian Splatting Vacuum
JunEn Low, Maximilian Adang, Javier Yu +2
We propose a new simulator, training approach, and policy architecture, collectively called SOUS VIDE, for end-to-end visual drone navigation. Our trained policies exhibit zero-sho…