1 citations · 1 across the 6 of their papers we have counts for
7 papers
Gripper-aware Vision Language Action Models
Hanyi Zhang, Zihong Luo, Tianyu Li +16
Vision language action models (VLAs) have advanced general purpose robotic grasping and manipulation by enabling robots to interpret visual observations and natural language instru…
Beyond Visual Grasping: Benchmarking Complex Grasping from Detection to Execution
Hanyi Zhang, Khang Nguyen, Charith Munasinghe +10
Robust robotic grasping remains a fundamental challenge for complex real-world applications. Recent advances in large-scale models demonstrate promising capabilities for reasoning…
AffordMatcher: Affordance Learning in 3D Scenes from Visual Signifiers
Nghia Vu, Tuong Do, Khang Nguyen +8
Affordance learning is a complex challenge in many applications, where existing approaches primarily focus on the geometric structures, visual knowledge, and affordance labels of o…
Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies
Mumuksh Tayal, Manan Tayal, Ravi Prakash
Offline safe reinforcement learning (RL) seeks reward-maximizing policies from static datasets under strict safety constraints. Existing methods often rely on soft expected-cost ob…
Sequentially Teaching Sequential Tasks : Teaching Robots Long-horizon Manipulation Skills
Zlatan Ajanović, Ravi Prakash, Leandro de Souza Rosa +1
Learning from demonstration has proved itself useful for teaching robots complex skills with high sample efficiency. However, teaching long-horizon tasks with multiple skills is ch…
Impedance Primitive-augmented Hierarchical Reinforcement Learning for Sequential Tasks
Amin Berjaoui Tahmaz, Ravi Prakash, Jens Kober
This paper presents an Impedance Primitive-augmented hierarchical reinforcement learning framework for efficient robotic manipulation in sequential contact tasks. We leverage this…
Generalizable Motion Policies through Keypoint Parameterization and Transportation Maps
Giovanni Franzese, Ravi Prakash, Cosimo Della Santina +1
Learning from Interactive Demonstrations has revolutionized the way non-expert humans teach robots. It is enough to kinesthetically move the robot around to teach pick-and-place, d…