Bi-MoDe: Bilateral Control-based Imitation Learning via Modifier-Conditioned Decoding for Modulation of Execution Speed and Contact Intensity
A framework that lets the operator specify how a learned task is executed — execution speed and contact intensity — at inference time. The constrained latent aligned with modifier directives is injected into every layer of the Transformer action decoder via adaLN-Zero, substantially improving physical directive following on a real-world whiteboard wiping task.
Bi-AQUA: Bilateral Control-Based Imitation Learning for Underwater Robot Arms via Lighting-Aware Action Chunking with Transformers
The world’s first bilateral control-based imitation learning for underwater environments realized in the real world. Robot learning model based on underwater/above-water images, lighting, position, and force information, with autonomous operation via position and force control.
Bilateral Control-Based Multimodal Hierarchical Imitation Learning via Subtask-Level Progress Rate and Keyframe Memory for Long-Horizon Contact-Rich Robotic Manipulation
Bilateral control-based multimodal hierarchical imitation learning framework for long-horizon contact-rich manipulation. Integrates keyframe memory with subtask-level progress rate to stabilize hierarchical coordination; demonstrates consistent improvements over flat and ablated variants on unimanual and bimanual real-robot tasks.
Bi-VLA: Bilateral Control-Based Imitation Learning via Vision-Language Fusion for Action Generation
Bilateral control-based imitation learning via language-guided visual information adjustment. Achieves action generation by integrating natural language instructions with visual information.
Bi-LAT: Bilateral Control-Based Imitation Learning via Natural Language and Action Chunking with Transformers
Bilateral control-based imitation learning combining natural language and action chunking. Achieves flexible robot manipulation with force consideration through language instructions.
ALPHA-α and Bi-ACT Are All You Need: Importance of Position and Force Control and Information in Imitation Learning for Unimanual and Bimanual Robotic Manipulation
Proposes the importance of position and force control and information in low-cost single-arm and dual-arm robot manipulation for research in daily tasks. Demonstrates the effectiveness of bilateral control-based imitation learning Bi-ACT. Examples include egg transportation and bottle cap opening.
DABI: Evaluation of Data Augmentation Methods Using Downsampling in Bilateral Control-Based Imitation Learning with Images
Evaluation of data augmentation methods using downsampling in bilateral control-based imitation learning with images. Addresses different sensor frequencies.
Bi-ACT: Bilateral Control-Based Imitation Learning via Action Chunking with Transformer
Proposes Bi-ACT, which fuses the well-known Action Chunking with Transformers (ACT) from ALOHA/ACT with bilateral control-based imitation learning using position and force control. The importance of force control for generalization to unseen objects became clear.
LfDT: Learning Dual-Arm Manipulation from Demonstration Translated from a Human and Robotic Arm
Proposes LfDT, a framework for dual-arm coordination tasks that performs domain translation from human-robot demonstration data pairs to robot-robot demonstration data, enabling imitation learning. Cross-domain correspondence in a CycleGAN-like manner.
MR-GLi: Mixed Reality-Based Gripper-Linked Overlays for Underwater Robot Arm Teleoperation via Bilateral Control
An MR interface for underwater robot arm teleoperation that spatially registers the reaction torque indicator and wrist-camera image to the gripper. In a within-subject study with 20 participants, it significantly reduced the perceived burden of gaze shifting while maintaining torque-regulation performance, workload, and usability.
Mixed Reality-Based Robot Navigation Interface Using Spatial Pointing and Speech with Large Language Model
Mixed Reality (MR)-based robot navigation interface that replaces complex hand gestures with a natural, multimodal interface combining spatial pointing and LLM-based speech interaction. Significantly reduces task completion time and workload compared to conventional gesture-based MR systems.
MRReP: Mixed Reality-based Hand-drawn Reference Path Editing Interface for Mobile Robot Navigation
A mixed-reality interface for mobile robots in which users draw a hand-drawn reference path (HRP) on the floor; a custom planner integrates it into the ROS 2 navigation stack for autonomous driving. In a within-subject study against a conventional 2D map interface, MRReP improved path-specification accuracy, usability, and perceived workload, and enabled more stable route specification in the physical environment.
MR-UBi: Mixed Reality-Based Underwater Robot Arm Teleoperation System with Reaction Torque Indicator via Bilateral Control
Mixed Reality (MR)-based underwater robot arm teleoperation system with reaction torque indicator for underwater environments. Achieves intuitive operation via bilateral control.
EmoLo: Emotion-Inspired Expressive Locomotion via Single-Policy Reinforcement Learning on Low-Cost Bipedal Robots
Style-conditioned reinforcement learning for emotion-inspired expressive locomotion on the low-cost bipedal robot Open Duck Mini V2. Three discrete styles—Happy, Neutral, and Sad—are served by a single shared policy, with sim-to-real transfer via ONNX inference on hardware.
D3DWA: Adaptive Weight and Prediction-Horizon for Dynamic Window Approach via Dueling Double Deep Q-Network
Adaptive local path planning that jointly selects the DWA evaluation weights and prediction horizon at every control step using a Dueling Double Deep Q-Network. It reached the goal in all eight simulated environments and all three real-robot configurations.
DQDWA: Local Path Planning: Dynamic Window Approach With Q-Learning Considering Congestion Environments for Mobile Robot
Local path planning for mobile robots considering congested environments via dynamic DWA parameter adjustment based on reinforcement learning (Q-Learning).
