Alessandro Roncone

Robots Influencing Humans to Reveal their Goals during Collaboration and Competition

Debasmita Ghose, Oz Gitelson, Michal Lewkowicz +3

We propose a unified strategy for fast goal inference in human-robot interaction. The core idea is to drive the human toward Critical Decision Points (CDPs)-states where competing…

GOLEM: Modular Humanoid Autonomy Towards Electric Vehicle Battery Disassembly

Max Conway, William Xie, Allen Devaraj +14

Disassembling end-of-life electric vehicle (EV) battery packs is dull and dangerous work, performed almost entirely by humans. We present GOLEM (Generalized Open Library of Embodie…

Environment Design for Reliable Shared Autonomy with Probabilistic Guarantees

Yi-Shiuan Tung, Himanshu Gupta, Gyanig Kumar +3

Shared autonomy enables humans and robots to collaboratively perform tasks by combining human input with autonomous assistance. Most prior work focuses on improving intent inferenc…

Risk-Aware Preference Learning for Stochastic Outcomes

Yi-Shiuan Tung, Yuni Wu, Wei Jiang +2

Learning reward functions from human preferences is a widely used approach for aligning robot behavior with user expectations in human-robot interaction. Most existing approaches a…

Efficient Multi-Robot Motion Planning with Precomputed Translation-Invariant Edge Bundles

Himanshu Gupta, Paul Motter, Aritra Chakrabarty +5

Solving multi-robot motion planning (MRMP) requires generating collision-free kinodynamically feasible trajectories for multiple interacting robots. We introduce Kinodynamic Transl…

Physically Accurate Rigid-Body Dynamics in Particle-Based Simulation

Ava Abderezaei, Nataliya Nechyporenko, Joseph Miceli +2

Robotics demands simulation that can reason about the diversity of real-world physical interactions, from rigid to deformable objects and fluids. Current simulators address this by…

Moving On, Even When You're Broken: Fail-Active Trajectory Generation via Diffusion Policies Conditioned on Embodiment and Task

Gilberto G. Briscoe-Martinez, Yaashia Gautam, Rahul Shetty +3

Robot failure is detrimental and disruptive, often requiring human intervention to recover. Our vision is 'fail-active' operation, allowing robots to safely complete their tasks ev…

FAME: Force-Adaptive RL for Expanding the Manipulation Envelope of a Full-Scale Humanoid

Niraj Pudasaini, Yutong Zhang, Jensen Lavering +4

Maintaining balance under external hand forces is critical for humanoid bimanual manipulation, where interaction forces propagate through the kinematic chain and constrain the feas…

CRED: Counterfactual Reasoning and Environment Design for Active Preference Learning

Yi-Shiuan Tung, Gyanig Kumar, Wei Jiang +2

As a robot's operational environment and tasks to perform within it grow in complexity, the explicit specification and balancing of optimization objectives to achieve a preferred b…

ShelfAware: Real-Time Semantic Localization in Quasi-Static Environments with Low-Cost Sensors

Shivendra Agrawal, Jake Brawer, Ashutosh Naik +2

Many indoor workspaces are quasi-static: their global geometric layout is stable, but local semantics change continually, producing repetitive geometry, dynamic clutter, and percep…

BBoE: Leveraging Bundle of Edges for Kinodynamic Bidirectional Motion Planning

Srikrishna Bangalore Raghu, Alessandro Roncone

In this work, we introduce BBoE, a bidirectional, kinodynamic, sampling-based motion planner that consistently and quickly finds low-cost solutions in environments with varying obs…

Dynamics-Compliant Trajectory Diffusion for Super-Nominal Payload Manipulation

Anuj Pasricha, Joewie Koh, Jay Vakil +1

Nominal payload ratings for articulated robots are typically derived from worst-case configurations, resulting in uniform payload constraints across the entire workspace. This cons…

MorphIt: Flexible Spherical Approximation of Robot Morphology for Representation-driven Adaptation

Nataliya Nechyporenko, Yutong Zhang, Sean Campbell +1

What if a robot could rethink its own morphological representation to better meet the demands of diverse tasks? Most robotic systems today treat their physical form as a fixed cons…

Counterfactual Reasoning and Environment Design for Active Preference Learning

Yi-Shiuan Tung, Bradley Hayes, Alessandro Roncone

For effective real-world deployment, robots should adapt to human preferences, such as balancing distance, time, and safety in delivery routing. Active preference learning (APL) le…

Employing Laban Shape for Generating Emotionally and Functionally Expressive Trajectories in Robotic Manipulators

Srikrishna Bangalore Raghu, Clare Lohrmann, Akshay Bakshi +4

Successful human-robot collaboration depends on cohesive communication and a precise understanding of the robot's abilities, goals, and constraints. While robotic manipulators offe…

Exploring How Non-Prehensile Manipulation Expands Capability in Robots Experiencing Multi-Joint Failure

Gilberto Briscoe-Martinez, Anuj Pasricha, Ava Abderezaei +4

This work explores non-prehensile manipulation (NPM) and whole-body interaction as strategies for enabling robotic manipulators to conduct manipulation tasks despite experiencing l…

Towards Super-Nominal Payload Handling: Inverse Dynamics Analysis for Multi-Skill Robotic Manipulation

Anuj Pasricha, Alessandro Roncone

Motion planning for articulated robots has traditionally been governed by algorithms that operate within manufacturer-defined payload limits. Our empirical analysis of the Franka E…

Clutter-Aware Spill-Free Liquid Transport via Learned Dynamics

Ava Abderezaei, Anuj Pasricha, Alex Klausenstock +1

In this work, we present a novel algorithm to perform spill-free handling of open-top liquid-filled containers that operates in cluttered environments. By allowing liquid-filled co…