176 citations · 341 across the 23 of their papers we have counts for
24 papers
PHASE: Compliance-Enabled Tactile Phase Retrieval for Few-Shot Insertion Learning
Jeremy Siburian, Cristian C. Beltran-Hernandez, Tatsuya Matsushima +3
Contact-rich assembly tasks such as peg-in-hole insertion remain difficult to learn from limited demonstrations. While retrieval-augmented imitation learning, which augments target…
CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding
Yuri Ishitoya, Jeremy Siburian, Masashi Hamaya +3
Vision-language-action models (VLAs) inherit semantic capabilities from pretrained VLMs, yet large-scale post-training on robot data and architectural modifications can reshape the…
Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning
Koki Yamane, Cristian C. Beltran-Hernandez, Steven Oh +2
Fast execution of contact-rich manipulation is critical for practical deployment, yet providing fast demonstrations for imitation learning (IL) remains challenging: humans cannot d…
Simulation-Driven Evolutionary Motion Parameterization for Contact-Rich Granular Scooping with a Soft Conical Robotic Hand
Yongliang Wang, Cristian C. Beltran-Hernandez, Tomoya Takahashi +1
Tool-based scooping is vital in robot-assisted tasks, enabling interaction with objects of varying sizes, shapes, and material states. Recent studies have shown that flexible, reco…
A Soft Wrist with Anisotropic and Selectable Stiffness for Robust Robot Learning in Contact-rich Manipulation
Steven Oh, Tomoya Takahashi, Cristian C. Beltran-Hernandez +2
Contact-rich manipulation tasks in unstructured environments pose significant robustness challenges for robot learning, where unexpected collisions can cause damage and hinder poli…
Stable In-hand Manipulation for a Lightweight Four-motor Prosthetic Hand
Yuki Kuroda, Tomoya Takahashi, Cristian C. Beltran-Hernandez +2
Electric prosthetic hands should be lightweight to decrease the burden on the user, shaped like human hands for cosmetic purposes, and designed with motors enclosed inside to prote…
Tactile Memory with Soft Robot: Robust Object Insertion via Masked Encoding and Soft Wrist
Tatsuya Kamijo, Mai Nishimura, Nodoka Shibasaki +3
Tactile memory, the ability to store and retrieve touch-based experience, is critical for contact-rich tasks such as key insertion under uncertainty. To replicate this capability,…
SCU-Hand with Integrated Single-Sheet Valve: A Funnel-Shaped Robotic Hand for Milligram-Scale Powder Handling
Tomoya Takahashi, Yusaku Nakajima, Cristian Camilo Beltran-Hernandez +5
Laboratory Automation (LA) has the potential to accelerate solid-state materials discovery by enabling continuous robotic operation without human intervention. While robotic system…
Learning-based Cooperative Robotic Paper Wrapping: A Unified Control Policy with Residual Force Control
Rewida Ali, Cristian C. Beltran-Hernandez, Weiwei Wan +1
Human-robot cooperation is essential in environments such as warehouses and retail stores, where workers frequently handle deformable objects like paper, bags, and fabrics. Coordin…
PLEXUS Hand: Lightweight Four-Motor Prosthetic Hand Enabling Precision-Lateral Dexterous Manipulation
Yuki Kuroda, Tomoya Takahashi, Cristian C Beltran-Hernandez +2
Electric prosthetic hands should be lightweight to decrease the burden on the user, shaped like human hands for cosmetic purposes, and have motors inside to protect them from damag…
Robust and Resilient Soft Robotic Object Insertion with Compliance-Enabled Contact Formation and Failure Recovery
Mimo Shirasaka, Cristian C. Beltran-Hernandez, Masashi Hamaya +1
Object insertion tasks are prone to failure under pose uncertainty and environmental variation, often requiring manual fine-tuning or controller retraining. We present a novel appr…
Grounded Vision-Language Interpreter for Long-Horizon Bimanual Task and Motion Planning
Jeremy Siburian, Keisuke Shirai, Cristian C. Beltran-Hernandez +3
While recent advances in vision-language models have accelerated language-guided robot planning, their black-box nature lacks the safety guarantees and interpretability crucial for…
SCU-Hand: Soft Conical Universal Robotic Hand for Scooping Granular Media from Containers of Various Sizes
Tomoya Takahashi, Cristian C. Beltran-Hernandez, Yuki Kuroda +3
Automating small-scale experiments in materials science presents challenges due to the heterogeneous nature of experimental setups. This study introduces the SCU-Hand (Soft Conical…
KeyMPs: One-Shot Vision-Language Guided Motion Generation by Sequencing DMPs for Occlusion-Rich Tasks
Edgar Anarossi, Yuhwan Kwon, Hirotaka Tahara +6
Dynamic Movement Primitives (DMPs) provide a flexible framework wherein smooth robotic motions are encoded into modular parameters. However, they face challenges in integrating mul…
Learning Diffusion Policies from Demonstrations For Compliant Contact-rich Manipulation
Malek Aburub, Cristian C. Beltran-Hernandez, Tatsuya Kamijo +1
Robots hold great promise for performing repetitive or hazardous tasks, but achieving human-like dexterity, especially in contact-rich and dynamic environments, remains challenging…
Robotic Object Insertion with a Soft Wrist through Sim-to-Real Privileged Training
Yuni Fuchioka, Cristian C. Beltran-Hernandez, Hai Nguyen +1
This study addresses contact-rich object insertion tasks under unstructured environments using a robot with a soft wrist, enabling safe contact interactions. For the unstructured e…
Learning Variable Compliance Control From a Few Demonstrations for Bimanual Robot with Haptic Feedback Teleoperation System
Tatsuya Kamijo, Cristian C. Beltran-Hernandez, Masashi Hamaya
Automating dexterous, contact-rich manipulation tasks using rigid robots is a significant challenge in robotics. Rigid robots, defined by their actuation through position commands,…
SliceIt! -- A Dual Simulator Framework for Learning Robot Food Slicing
Cristian C. Beltran-Hernandez, Nicolas Erbetti, Masashi Hamaya
Cooking robots can enhance the home experience by reducing the burden of daily chores. However, these robots must perform their tasks dexterously and safely in shared human environ…
Symmetry-aware Reinforcement Learning for Robotic Assembly under Partial Observability with a Soft Wrist
Hai Nguyen, Tadashi Kozuno, Cristian C. Beltran-Hernandez +1
This study tackles the representative yet challenging contact-rich peg-in-hole task of robotic assembly, using a soft wrist that can operate more safely and tolerate lower-frequenc…
Vision-Language Interpreter for Robot Task Planning
Keisuke Shirai, Cristian C. Beltran-Hernandez, Masashi Hamaya +6
Large language models (LLMs) are accelerating the development of language-guided robot planners. Meanwhile, symbolic planners offer the advantage of interpretability. This paper pr…
Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Study
Cristian C. Beltran-Hernandez, Damien Petit, Ixchel G. Ramirez-Alpizar +1
The Reinforcement Learning (RL) paradigm has been an essential tool for automating robotic tasks. Despite the advances in RL, it is still not widely adopted in the industry due to…
Robotic Imitation of Human Assembly Skills Using Hybrid Trajectory and Force Learning
Yan Wang, Cristian C. Beltran-Hernandez, Weiwei Wan +1
Robotic assembly tasks involve complex and low-clearance insertion trajectories with varying contact forces at different stages. While the nominal motion trajectory can be easily o…
Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach
Cristian C. Beltran-Hernandez, Damien Petit, Ixchel G. Ramirez-Alpizar +1
Industrial robot manipulators are playing a more significant role in modern manufacturing industries. Though peg-in-hole assembly is a common industrial task which has been extensi…
Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled Robots
Cristian Camilo Beltran-Hernandez, Damien Petit, Ixchel G. Ramirez-Alpizar +4
Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because…