Excited to announce that registration is open for the PhyRC 2027 Challenge, our physical robotic caregiving competition, now co-organized by the @EmpriseLab at @Cornell_Bowers and the RIRO Lab at KAIST. One track: mobile manipulation for robot-assisted dressing. A seated manikin, a closed-topology T-shirt, and a robot that has to thread it over the body and dress both arms. Phase 1 runs in NVIDIA Isaac Sim through 15 Dec. Top teams move to real robots in 2027. We thank @hellorobotinc for generously sponsoring a Stretch 4 robot for the winning team! 56 teams from 17 countries entered the inaugural edition in 2025, which finished with live finals at ICRA 2025. Registration closes 15 Oct: emprise.cs.cornell.edu/phyrc…

Check out the RAG-Diff work by @Nekovowo and the team at #RSS2026. What I find most interesting about RAG-Diff is its treatment of user preferences as two distinct forms of information. Some are explicit (language requests, force limits, regions to avoid, or spatial goals). Others are implicit in prior behavior: what did this person prefer in a similar situation? RAG-Diff handles both at test time. PrefMem retrieves a relevant state–action snippet and its constraint annotation. I-Atten injects the snippet into the frozen diffusion policy’s cross-attention, while gradient-based value guidance steers denoising toward satisfying the explicit constraint. This combination enables a frozen policy to adapt online across interaction, affordance, spatial, and semantic preferences. @EmpriseLab @Cornell_CS @Cornell_Bowers

Proud of @madrish02 and the entire team's work on TACTIC. What I find most compelling about TACTIC is that contact is not treated as just another sensor input or a penalty term, it is built into every layer of the planning stack. In the representation, RGB-D, proprioception, and distributed tactile sensing are fused through a proximity mask that focuses the model on the interaction geometry that actually matters: where contact is happening, and where it may happen next. In sampling, contact Jacobians shape MPPI exploration, biasing candidate actions toward directions that can meaningfully regulate forces and steering unsafe samples away from high-force contacts. In prediction, hybrid rollouts combine learned interaction dynamics with analytical robot kinematics: learn what is hard to model, while preserving the structure we already know. Interestingly, this hybrid variant idea that adds a kinematics-based goal cost during planning also improves planning with other learned world models, including V-JEPA2 on Reach and DINO-WM on Granular. And in scoring, trajectories are evaluated not only for task progress, but also for contact evolution, force regulation, joint limits, and collisions. That contact-centricity across representation, sampling, prediction, and scoring is, to me, the most exciting part of TACTIC. Very proud of the team and excited to see this work at #RSS2026! @EmpriseLab @Cornell_Bowers @Cornell_CS

EmPRISE Lab will have a strong presence at #RSS2026, with 5 main-conference papers plus workshop papers, talks, and panels. Here’s where to find our main conference work: Monday, July 13: Session "World Models and Memory" Check out RAG-Diff, led by @Nekovowo. RAG-Diff is a retrieval-augmented framework for adapting a frozen diffusion policy at test time to dynamic, personalized user preferences in caregiving-like environments. It retrieves relevant examples and constraints from a memory bank (PrefMem), using in-place attention re-computation (I-Atten) for implicit preferences and value guidance for explicit preferences. Website: emprise.cs.cornell.edu/rag-d… Tuesday, July 14: Session "Manipulation 2" Check out TACTIC, led by @madrish02. TACTIC is a receding-horizon controller for whole-arm manipulation. TACTIC combines vision and distributed tactile sensing in a contact-centric representation, then uses contact-aware action sampling and a hybrid latent/analytical predictive model to balance task progress with whole-arm force regulation. Website: emprise.cs.cornell.edu/tacti… Wednesday, July 15: Session "HRI" @ZhanxinWu0725 will present E2Care, a context-aware physical caregiving system designed to operate safely across changing tasks, environments, and robot embodiments. It treats caregiving primitives as interaction templates that can be reshaped online according to context. Website: emprise.cs.cornell.edu/e2car… @jiaying_fang0 will present E-MPC: An Engagement-Aware Human-in-the-Loop Framework for Robotic Systems. E-MPC recognizes that task success alone is insufficient for human-centered robots: users may still feel passive, disengaged, or overloaded. It jointly reasons about task success, engagement, and workload to decide when and how to involve the user. Website: emprise.cs.cornell.edu/empc Finally, check out “Knowing When Not to Help," led by @RealZiangLiu and Yunting Yan, exploring a central challenge in robot-assisted caregiving: providing help only when it is needed. Its Bayesian active-learning framework combines biomechanical models, clinical knowledge, and LLM-guided priors to learn user-specific reachability online and support “just-right” assistance while maintaining reliable task performance. Website: emprise.cs.cornell.edu/human… You can also find us at the workshops: • Rethinking What It Means to Be Safe for Generalist Robots • Beyond the Lab: Rethinking Methods for Human Behavior Monitoring and Modeling in In-the-Wild HRI • Towards Robust Execution of Long-Horizon Whole-Body Control Tasks • Planning and Control with Imperfect Sensors and Perception Due to visa complications, Ruolin, Rishabh, and Yunting will not be attending in person. Zhanxin, Jiaying, Joyce Yang, and I will be there, and will also present on their behalf. Come say hi! :-) @EmpriseLab @Cornell_CS @Cornell_Bowers

A special day for our lab. My first cohort of PhD students, @rohanbbanerjee and @rkjenamani, reached this graduation milestone this year. Rohan’s work advances failure recovery for assistive robots, and Rajat’s work advances long-horizon robot-assisted feeding in real homes. Also very proud of all our graduating lab members, including our undergraduates, some of whom are in this photo (Shao Stassen, Bodong Liu, Bohan Yang, and Eric Hu), and others who are not (Nayoung (Sarah) Ha, Xiaoyu (Sherry) Yang, Rolando Rodriguez, Andres Blanco, and Tina Cheng). Congratulations to all of you. I’ll be cheering for you in your exciting next chapters. @EmpriseLab @Cornell_CS

Check out @rohanbbanerjee's work on a confidence- and human workload-aware robot failure recovery methods for modular robot systems in #HRI2026. In a robot system, failures can happen due to reasons spanning from a variety of errors in perception, planning, to controls. In user-facing settings, it only makes sense to leverage the user (already present) to recover from errors that the robot cannot recover autonomously. However, instead of treating users as "tools" to improve robot performance whenever the robot is uncertain, let's consider human factors central to these algorithms. In this paper, @rohanbbanerjee considers one such user variable: user querying workload. The key insight is that through a variety of "module selectors" to select "what to query" and "querying algorithms" to select "when to query", it is possible to maintain the balance between robot task performance and user workload for a wide variety of modular human-robot collaboration settings, and this leads to higher user satisfaction. In addition to providing guidelines for failure recovery (which algorithms to use for module selection and querying) for a wide variety of robot applications that vary in their complexity (number of modules, module redundancies, confidence scores, noisy experts, user workload etc.), @rohanbbanerjee has deployed his algorithms on a real robot for the robot-assisted feeding application, and evaluated them with users through 3 different real-robot user studies with 22 people including 2 individuals with mobility limitations in their real homes. @EmpriseLab @Cornell_Bowers @HRI_Conference Project website with paper and video (needs sound for narration): emprise.cs.cornell.edu/modul…

Very excited to be a part of this assistive mobility + manipulation effort!

Physical caregiving is one of robotics' hardest frontiers: it is contact-rich, physically intensive, long-horizon, safety-critical, and full of deformable objects. Physical caregiving tasks such as bathing, dressing, transferring, toileting, and grooming require professional training and considerable practical experience. Yet, no existing dataset captures how expert caregivers perceive, interact, and adapt in real-time when performing these tasks, in a form that robots can learn from. ✨ We introduce OpenRoboCare at #IROS2025, the first expert-collected, multi-task, multimodal dataset for physical robot caregiving, featuring: 🩺 21 expert occupational therapists demonstrating caregiving procedures 🛠️ 15 caregiving tasks across 5 Activities of Daily Living (bathing, dressing, transferring, toileting, grooming) 🧍 2 hospital-grade manikins for safety and repeatability 🎥 5 synchronized sensing modalities: RGB-D, pose tracking, eye gaze, tactile sensing, and expert task & action annotations 📂 315 sessions · 19.8 hrs · 31,185 samples Beyond raw data, OpenRoboCare distills core physical caregiving insights: - 3 core principles followed by occupational therapists: pre-positioning, anticipation of body mechanics, and task efficiency. - 4 key physical techniques: the bridge strategy, segmental rolling, wheelchair recline, and stabilization of key control points. - Quantitative patterns in task duration, predictive gaze behavior that precedes physical contact, and the timing, magnitude, and spatial distribution of contact forces across body regions and task phases. The dataset will be made openly accessible through the AWS Open Data Sponsorship Program soon. 🌐 Check out our project website for more visuals and insights: emprise.cs.cornell.edu/robo-… This work is led by: @xiaoyul14, @RealZiangLiu, and Kelvin Lin. This is a collaboration with Harold Soh's group from NUS and @DimitropoulouDr from CUIMC. @EmpriseLab @Cornell_CS @IROS2025 @awscloud