Paper Daily · Embodied AI (English)

Pushing Dexterity and Safety for In-Hand Object Pose Reaching That Works Remarkably Well

Thu, 29 Oct 2026 08:00:00 +0800

Skills learned by multi-fingered hands mostly stop at simple grasps or spinning an object in place. For harder in-hand moves the most common failure is dropping the object, yet earlier work rarely counts drops. This paper has a LEAP hand move a held object to almost any position and orientation, with random wrist orientations, through one goal after another. The main policy is trained entirely in…

Embodied AI · Score 4.75 · Ian Chuang, Hiya Shah, Vamsi Chilamkur, Iman Soltani, Claire Tomlin · Source

OpenJaws: An Open Hardware UMI Framework with Stereo Localization for Generalist Manipulation

Thu, 29 Oct 2026 08:00:00 +0800

Collecting robot demonstrations in the wild with a handheld gripper (UMI) is now common, but how good the action labels are depends on one step: recovering the gripper trajectory from the wrist-camera video. The original UMI uses a monocular camera plus an IMU (inertial measurement unit); a single camera cannot see true scale, so it also needs a pre-built map and a fiducial marker to stay stable.…

Embodied AI · Score 4.65 · Deep dive · 40 min · Adithyavairavan Murali, Yash Oza, Karanbir Chahal, Neel Jawale, Silas De Vries, Hesam Rabeti, Erwin… · Source

Sequential Asymmetric Imitation for Learning Coupled Robot Policies

Thu, 29 Oct 2026 08:00:00 +0800

When two mobile bimanual robots work together through a shared object, failures usually come not from weak individual skills but from bad timing, such as not waiting or yielding when they should, or still pulling when they should let go. The usual approach needs two people teleoperating at once, which is costly and hard to synchronize. This paper's SAI needs only one operator and works in three s…

Embodied AI · Score 4.40 · Ranpeng Qiu, Yincong Chen, Zihao Li, Yanan Zhou, Guoqiang Ren, Weiming Zhi · Source

DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination

Thu, 29 Oct 2026 08:00:00 +0800

When a dexterous hand does contact-rich manipulation, the fingers often hide the object from the camera, and a vision-only VLA model struggles to handle contact and force. Earlier work has started adding touch, but mostly concatenates tactile and visual features regardless of whether the hand is touching anything. DeCAL lets touch take part according to contact: less when nothing is touched, more…

Embodied AI · Score 4.40 · Yankai Fu, Ning Chen, Junkai Zhao, Heng Zhang, Guocai Yao, Pengwei Wang, Zhongyuan Wang, Shanghang… · Source

VLA-Stem: A Lightweight Safety Mechanism for Vision-Language-Action Models via Reinforcement Learning

Thu, 29 Oct 2026 08:00:00 +0800

VLA models (large models that read images and instructions and output robot actions directly) are good at grasping and placing on command, but most are trained on tables without obstacles; add a box nearby or let something move in, and they drive straight into it. Fine-tuning the VLA on obstacle-avoidance data is the obvious fix, but such data is hard to collect and can hurt the original task ski…

Embodied AI · Score 4.40 · Bojun Zou, Wenhao Yu, Jiaohao Li, Jiaxuan Lin, Guanting Ye, Haohan Geng, Weihao Yu, Yanyong Zhang,… · Source

TOPS: Reshaping Flow Source and Transport for Multi-Task Robot Manipulation

Thu, 29 Oct 2026 08:00:00 +0800

Multi-task robot policies that generate actions with Flow Matching usually start every task from the same cloud of Gaussian noise and move along a straight path to an action. But opening a drawer and stacking blocks need very different motions, and with one shared start and path it is hard to learn every task well. TOPS keeps the network unchanged and alters only these two things. It traces exper…

Embodied AI · Score 4.40 · Tuo An, Chuhao Zhou, Jiahua Dong, Jingliang Li, Jiaqi Bai, Jindou Jia, Jianfei Yang · Source

CARD: Contact-Anchored Retargeting for Dexterous Manipulation

Thu, 29 Oct 2026 08:00:00 +0800

The easiest way to teach a dexterous robot hand is to convert human hand demonstrations directly into robot actions, which is called retargeting. But existing methods only match fingertip positions and joint angles and ignore whether the hand touches the object and how hard, so the robot hand often fails to stay in contact where it should. CARD first records, for each frame, which parts of the hu…

Embodied AI · Score 4.40 · Cunxi Dai, Botao He, Francois R Hogan, Wei Pan, Rao Fu, Wen-Han Hsieh, Ruoshi Liu, Jitendra Malik,… · Source

GRAFT: Geometry-Aware Residual Adaptation for Foundation Trackers

Thu, 29 Oct 2026 08:00:00 +0800

Humanoid behavior foundation models (BFMs, general tracking policies that turn a reference motion into whole-body joint commands) are mostly trained on flat-ground motion and tend to fall on stairs, gaps and platforms. The usual fix is to retrain or fine-tune the whole model together with perception, which is costly and can damage what it already does well on flat ground. Xiaomi Robotics Lab's GR…

Embodied AI · Score 4.40 · Liang Li, Guoyao Zhang, Xingyu Chen, Sikai Wu, Qianyang Wu, Yuhang Lin, Renjing Xu, Mingliang Zhou,… · Source

When Do Discrete Actions Make Good Continuous Controllers?

Thu, 29 Oct 2026 08:00:00 +0800

Large language models predict discrete tokens, yet robot foundation models almost all predict actions as continuous vectors, on the assumption that discretizing loses precision. This paper compares the two systematically, in simulation and on real robots, for single-task policies and multi-task VLAs, and finds that the most naive discrete scheme, cutting each action dimension into evenly spaced b…

Embodied AI · Score 4.40 · Zeji Yi, Soham Dasgupta, Xinyang Zhang, Koushil Sreenath, Guannan Qu, Xi Chen, Max Simchowitz, Guan… · Source

Frontier Models for Every Robot: Announcing our API beta

Thu, 29 Oct 2026 08:00:00 +0800

Physical Intelligence has opened its latest robot foundation models as an API beta: developers upload demonstration data, fine-tune a π model on compute it manages, and control their own robots through hosted cloud inference. Early users span many industries, from an autonomous wheel loader filling dump trucks at a real commercial site with no operator on board, to a dual-arm robot deburring part…

Embodied AI · Score 4.10 · Physical Intelligence · Source

Robot-DIFT: Correspondence-Sensitive Diffusion Features for Contact-Rich Robot Manipulation