Tactile sensors and haptic feedback for robotic manipulation.
Jiahe Pan, Stelian Coros, Jitendra Malik et al.
A primary bottleneck in contact-rich manipulation is the difficulty of collecting real-world data. Sim-to-real reinforcement learning offers a scalable alternative, but the simulation-reality gap prev
Patrick Rebling, Philipp Nenninger, Reiner Kriesten
The development of autonomous driving demands comprehensive testing in mixed-traffic scenarios involving vulnerable road users (VRUs), where purely artificial agents often fail to capture authentic human social negotiations.
Yu Hu, Fangzhou Zhao, Liang Chen et al.
Airborne torque reaction cannot recover takeoff errors beyond the wheel angular-momentum budget. DART-S applies ramp-face suspension preconditioning to change pitch, pitch rate, and wheel spin before liftoff, thereby shifting the queried state and altering the remaining authority budget.
Juan José García Cárdenas, Alperen Kenan, Hamidreza Raei et al.
Intuitive teleoperation interfaces are crucial for the safe and effective operation of robotic manipulators in challenging environments.
Kapalik Khanal, Biswash Khatiwada, Stephen Afrifa et al.
Changing a humanoid robot's manipulation policy should not require retraining the controller that maintains the robot's balance.
Jiajian Zheng, Bin Zhang, Wen Fan et al.
Multi-finger hands contact objects at different times, so a global close command can overload early-contact fingers while others are still moving. Dense tactile arrays address this problem but add hardware, calibration, bandwidth, and computation.
Ishtia Zahir, Eslam Saleh, Maryam Rezayati et al.
Textile-integrated capacitive sensors offer flexible and conformable tactile sensing for wearable electronics and human-robot interaction; however, the influence of yarn-level architecture on capacitive transduction characteristics remains insufficiently quantified.
Stefan Schneyer, Timo Bachmann, Maged Iskandar et al.
Contact-centric tasks on surfaces, ranging from inspection and cleaning to sanding and polishing, require robots to systematically cover the surface while maintaining stable contact.
Zhi Cao, Yining Hong, Jiaolong Yang et al.
Human demonstrations offer a scalable alternative to costly robot data collection for dexterous manipulation, but most human datasets lack force measurements.
Xiaofan Lu, Kaiji Huang, Jiahui Chen et al.
Curved tactile fingertips for dexterous manipulation must resolve fine contact geometry, distinguish normal and tangential loads, and capture transient signals.
Zhengyu Tao, Xin Li, Xin Wang
Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, yet their reliance on visual perception limits robustness in contact-rich environments, where critical physical interaction states may not be visually observable.
Ruitong Tian, Fang Xu, Noah B. Wilson et al.
Unlike conventional teleoperation, wearable interfaces allow operators to collect dexterous demonstrations through their own hand motions while directly interacting with task objects.
Ryohei Kobayashi, Kosei Isomoto, Yuga Yano et al.
We propose a framework that transfers tacit human subjective criteria to robotic systems for the appropriate grasping of deformable objects. Achieving such behavior is challenging because a semantic gap exists between qualitative human expectations and quantitative robotic measurements.
Dairu Liu, Zekun Qi, Jiayu Zeng et al.
Humanoid motion tracking is central to teleoperation and whole-body imitation, yet evaluation often disagrees with what people perceive in videos.
Seongjin Bien, D\'ebora Oliveira Makowski, Roberto Calandra et al.
Large-scale datasets are essential for training generalist robot control policies. Collecting real-world tactile data is costly and time-consuming, motivating the use of tactile simulations. However, current tactile simulators capture only overall contact geometry and miss fine details like texture.
Sang Min Kim, Alexander Alexiev, Tzu-Yuan Lin et al.
Robust dexterous grasping requires maintaining physical stability despite contacts interactively evolving across the entire hand. A precomputed force distribution can easily fail under object motion, modeling errors, or external disturbances.
Alperen Kenan, Paul Bremner, Manuel Giuliani
Learning from demonstration (LfD) provides a developmental framework through which robots can develop motor skills by observing and imitating human dynamics, reducing reliance on explicit programming to teach a skill to a robot.
Ruoxuan Feng, Yutong Chen, Ruihua Song et al.
Humans inherently understand the physical world through an active process. When sensory evidence is insufficient to infer physical properties, we naturally interact with the environment by deciding what information is missing, how to acquire it, and when sufficient evidence has been obtained.
Yibo Liu, Stanko Oparnica, Simon Shewchun-Jakaitis et al.
Contact-rich manipulation is fundamental in robotics but poses significant challenges due to uncertainties in relative poses, such as misalignments and small clearances in peg-in-hole tasks.
Jingyun Yang, Baiyu Shi, Timothy Yu et al.
A growing body of work suggests that tactile sensing gives robot policies contact information that complements vision in dexterous manipulation. However, visuo-tactile robot data remains scarce: dexterous demonstrations require teleoperating robots, which limits dataset scale.
Tianxing Chen, Wenwei Lin, Chuqiao Lyu et al.
Generalist robot policies benefit from large-scale visual pretraining but often struggle with contact-rich dexterous manipulation, where force, compliance, and incipient slip are hard to observe from images.
Ruiyi Hu, Yongqiang Zhao, Daniel Bak et al.
Hair stroking is common in daily grooming and personal care, and is also widely used in hair-product evaluation, motivating robots with similar physical interaction capabilities.
Huaze Liu, Zhenyu Wu, Jaehwi Jang et al.
Navigation in vision-denied environments is challenging for humanoid robots because proprioceptive odometry drifts and localization uncertainty accumulates rapidly.
Yitao Zhang, Hong Ying, Haoran Guo et al.
Robotic simulation and virtual reality increasingly require object assets that capture not only visual geometry but also the physical cues underlying tactile and thermal interaction.
Demetrious T. Kutzke, Junaed Sattar
Scuba divers are taught to control their depth to avoid rapid ascents and descents, which could result in serious injuries such as gas embolisms and barotrauma.
Jaehwi Jang, Zhaoyuan Gu, Alfred Cueva et al.
Whole-body humanoid manipulation of bulky, deformable, and shared-load objects requires distributed contact sensing and explicit force regulation, yet most imitation policies treat contact force only implicitly.
Mike Lambeta, Po-Wei Chou, Stephen Tian et al.
An affordable, compact, high-resolution vision-based tactile sensor optimized for in-hand manipulation with real-time fingertip deformation sensing.
Read these papers in order to build expertise in tactile.
Jiahe Pan, Stelian Coros et al. · 2026
Patrick Rebling, Philipp Nenninger et al. · 2026
Yu Hu, Fangzhou Zhao et al. · 2026
Juan José García Cárdenas, Alperen Kenan et al. · 2026
Kapalik Khanal, Biswash Khatiwada et al. · 2026
Jiajian Zheng, Bin Zhang et al. · 2026
…and 21 more papers.
