activity
20232026
most citedMoVEInt: Mixture of Variational Experts for Learning Human-Robot Interactions from Demonstrations

11 citations · 15 across the 13 of their papers we have counts for

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13 papers · 1 filter

cs.RO2026

Catch, Throw, Repeat: Planning for Human-Robot Partner Juggling

Jonathan Rainer Lippert, Kai Ploeger, Abir Chowdhury +3

Dynamic object exchange between humans and robots remains a challenging problem due to uncertainty in perception, timing, and contact-rich interaction. Human-robot juggling represe…

cs.RO2026

More Structure, Not More Capacity: Object-Centric Representations for Visuomotor Imitation Learning

Yi Li, Alexandre Chapin, Liming Chen +2

Robotic manipulation policies rely on pre-trained vision models that give either a global scene embedding or a dense patch grid. Both mix task-relevant and task-irrelevant features…

cs.RO2026

Robust Adaptive Backstepping Impedance Control of Robots in Unknown Environments

Reza Nazmara, Alap Kshirsagar, Jan Peters +1

This paper presents a Robust Adaptive Backstepping Impedance Control (RABIC) strategy for robots operating in contact-rich and uncertain environments. The proposed control strategy…

cs.RO2026

Task-Aware Bimanual Affordance Prediction via VLM-Guided Semantic-Geometric Reasoning

Fabian Hahne, Vignesh Prasad, Georgia Chalvatzaki +2

Bimanual manipulation requires reasoning about where to interact with an object and which arm should perform each action, a joint affordance localization and arm allocation problem…

cs.RO2025

Investigating the Effect of LED Signals and Emotional Displays in Human-Robot Shared Workspaces

Maria Ibrahim, Alap Kshirsagar, Dorothea Koert +1

Effective communication is essential for safety and efficiency in human-robot collaboration, particularly in shared workspaces. This paper investigates the impact of nonverbal comm…

cs.RO2025

In-Hand Object Pose Estimation via Visual-Tactile Fusion

Felix Nonnengießer, Alap Kshirsagar, Boris Belousov +1

Accurate in-hand pose estimation is crucial for robotic object manipulation, but visual occlusion remains a major challenge for vision-based approaches. This paper presents an appr…

cs.RO2025

Investigating Active Sampling for Hardness Classification with Vision-Based Tactile Sensors

Junyi Chen, Alap Kshirsagar, Frederik Heller +7

One of the most important object properties that humans and robots perceive through touch is hardness. This paper investigates information-theoretic active sampling strategies for…

cs.RO2024

TacEx: GelSight Tactile Simulation in Isaac Sim -- Combining Soft-Body and Visuotactile Simulators

Duc Huy Nguyen, Tim Schneider, Guillaume Duret +3

Training robot policies in simulation is becoming increasingly popular; nevertheless, a precise, reliable, and easy-to-use tactile simulator for contact-rich manipulation tasks is…

cs.RO2024★ 11 cited

MoVEInt: Mixture of Variational Experts for Learning Human-Robot Interactions from Demonstrations

Vignesh Prasad, Alap Kshirsagar, Dorothea Koert +3

Shared dynamics models are important for capturing the complexity and variability inherent in Human-Robot Interaction (HRI). Therefore, learning such shared dynamics models can enh…

cs.RO2024

What Matters for Active Texture Recognition With Vision-Based Tactile Sensors

Alina Böhm, Tim Schneider, Boris Belousov +6

This paper explores active sensing strategies that employ vision-based tactile sensors for robotic perception and classification of fabric textures. We formalize the active samplin…

cs.RO2024★ 1 cited

Transition State Clustering for Interaction Segmentation and Learning

Fabian Hahne, Vignesh Prasad, Alap Kshirsagar +4

Hidden Markov Models with an underlying Mixture of Gaussian structure have proven effective in learning Human-Robot Interactions from demonstrations for various interactive tasks v…

cs.RO2024★ 2 cited

Kinematically Constrained Human-like Bimanual Robot-to-Human Handovers

Yasemin Göksu, Antonio De Almeida Correia, Vignesh Prasad +4

Bimanual handovers are crucial for transferring large, deformable or delicate objects. This paper proposes a framework for generating kinematically constrained human-like bimanual…

cs.RO2023★ 1 cited

A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors

Shangding Gu, Alap Kshirsagar, Yali Du +3

Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL)…