Robot Learning and Wearable Interfaces in Pursuit of Robotic Caregivers
Abstract: Designing safe and reliable robotic assistance for caregiving is a grand challenge in robotics. A sixth of the United States population is over the age of 65 and more than 1 in 4 (over 70 million) adults in the United States reported having a disability in 2022. Robotic caregivers could positively benefit society; yet, physical [...]
Deep Abstraction Learning for Neuro-Symbolic World Modeling
Abstract: Modern foundation models have achieved remarkable progress by learning broad physical and semantic common sense from large-scale data. However, robots operating in open-ended environments require more than general knowledge alone: they must continually specialize in new tasks, environments, and experiences encountered during deployment. Given only limited deployment-time data, how can robots learn to solve substantially more—and [...]
Designing A Learning-Enabled Non-Anthropomorphic Robotic Hand Framework for Dexterous Manipulation
Abstract: As robots transition into unstructured human environments, achieving dexterous manipulation across diverse tasks becomes increasingly crucial yet remains challenging. This objective underscores the value of high-degree-of-freedom (DoF) robotic hands capable of versatile grasping and in-hand manipulation. Complementing the hardware, success in these settings also depends on intelligent and autonomous hand control strategies that provide [...]
Contact-Rich Manipulation with Vibro-Tactile Sensing
Abstract: Contact-rich manipulation requires reasoning about complex physical interactions. While vision and force sensing provide useful information, many interaction dynamics remain difficult to observe directly, especially under occlusion, rapid contact transitions, or distributed contact. This thesis explores how vibro-tactile sensing, also known as structure-born acoustic sensing, can serve as a practical and information-rich modality for [...]
RI PhD Speaking Qualifier / MSR Talk – Yuemin Mao
TBD
RI PhD Thesis Defense – Junyu Nan
Date: Tuesday May 19, 2026 Time: 1:00 - 3:00PM (EST) Location: NSH (Newell Simon Hall) 3305 Zoom Link Type: PhD Thesis Defense Title: Learning Geometric, Physical, and Semantic Priors for Embodied Planning and Control Abstract: Embodied intelligence requires perceiving, predicting, and acting in environments with an understanding of the geometric, semantic, and physical structure of the world. Recently, [...]
RI PhD Thesis Proposal – Renos Zabounidis
Date: May 20th, 2026 Time: 3:00 - 4:30 pm Location: NSH 4305 Zoom Link Type: RI PhD Thesis Proposal Who: Renos Zabounidis Title: Enforcing Neuro-Symbolic Structure in Deep Reinforcement Learning Abstract: Monolithic deep reinforcement learning trains a single network to learn vision, physics, planning, and control from reward alone. The result is poor sample efficiency, [...]
Scalable Imitation Learning for Robust Manipulation and Physical Human-Robot Interaction
Abstract: Robots in everyday human environments are expected to perform robust manipulation across cluttered, constrained, and physically interactive settings. This thesis studies how scalable simulation-based data generation can train vision-based imitation learning policies for robust zero-shot transfer to the real world. In the first part of this thesis, we study robotic manipulation in cluttered shelf [...]
Generalizable Neural Dynamics Modeling for Complex Deformable Object Manipulation
Abstract: Hair care is an essential daily activity, yet it remains inaccessible to individuals with limited mobility and challenging for autonomous robot systems due to the fine-grained physical structure and complex dynamics of hair. This thesis presents DYMO-Hair, a model-based robot hair care system. We introduce a novel dynamics learning paradigm that is suited for [...]
RI PhD Thesis Proposal – Anurag Ghosh
Date: May 21st, 2026 Time: 3:30 - 5:00 pm Room: NSH Room 4305 Zoom: https://cmu.zoom.us/j/98318417145 Type: RI PhD Thesis Proposal Who: Anurag Ghosh Title: Scaling Long-Tailed Driving Perception and Planning with In-the-Wild Videos Abstract: Closed-loop driving, where methods produce actions a simulator reacts to, remains largely tied to driving logs from instrumented fleets. Thus, reliably driving in rare-but-critical scenarios is [...]
RI PhD Thesis Defense – Kenneth Shaw
Date: Friday May 22 Time: 3:30PM - 4:30PM (EST) Location: NSH (Newell Simon Hall) 3305 ZOOM Link Title: Building Robot Hands and Teaching Dexterity Abstract: Our human hands are masterpieces of power and precision, capable of typing, hammering, or delicately using chopsticks. Yet most robots today still rely on simple two-finger grippers in controlled settings because dexterous hands are [...]
Toward Real-World Autonomous Off-Road Driving with Reinforcement Learning
Abstract: Off-road autonomous driving presents significant challenges such as navigating unmapped variable environments, traversing difficult terrain geometries such as steep slopes and ditches, and managing complex terrain dynamics. Addressing these challenges requires effective low-level adaptable control and long-horizon planning. Most existing methods utilize Model Predictive Control (MPC) methods such as Model Predictive Path Integral (MPPI), [...]
Explore and Exploit: Learning Policies for Efficient and Coordinated Active Search
Abstract: Robotic search is becoming a central capability in domains where the world is large, uncertain, and costly to inspect directly: search and rescue, environmental monitoring, surveillance, and infrastructure inspection. In these settings, the hard problem is not perception alone but the online sensing decision: where to look next as evidence arrives, while every motion [...]