Student Talks
Deformation-Aware Manipulation: Compliant and Geometric Approaches for Non-Anthropomorphic Hands
Abstract: Soft robot hands offer compelling advantages for manipulation tasks, including inherent safety through material compliance, robust adaptation to uncertain object geometries, and the ability to conform to complex shapes passively. However, these same properties create significant challenges for conventional sensing and control approaches. This talk presents approaches to bridging advances in geometric learning and [...]
Integrating Safety Across the Learning-Based Perception Pipeline: From Training to Deployment
Abstract: Robots operating in safety-critical environments must reason under uncertainty and novel situations. However, recent advances in data-driven perception have made it challenging to provide formal safety guarantees, particularly when systems encounter out-of-distribution or previously unseen inputs. For such systems to be safely deployed in the real world, we need to incorporate safety considerations alongside [...]
Toward Generalizable Interaction-aware Human Motion Prediction
Abstract: As autonomous robots are increasingly expected to operate in dynamic, human-centered environments, it is crucial to develop robot policies that ensure safe and seamless interactions with humans, all while allowing robots to complete their intended tasks efficiently. To achieve this, robots must be capable of making informed decisions that account for human preferences, ensuring [...]
Enhancing Reinforcement Learning with Error-Prone Language Models
The correct specification of reward models is a well-known challenge in reinforcement learning. Hand-crafted reward functions, which are usually sparse, often lead to inefficient or suboptimal policies, misalignment with user values, or difficulties in attributing credit or blame within multi-agent systems. Reinforcement learning from human feedback is a successful technique that can mitigate such issues [...]
Efficient Multi-Agent Motion Planning using Local Policies
Abstract: Teams of multiple robots working together can achieve challenging tasks like warehouse automation, search and rescue, and cooperative construction. However, finding efficient collision-free motions for all agents is extremely challenging as the complexity of the multi-agent motion planning (MAMP) problem grows exponentially with the number of agents. Multi-Agent Path Finding (MAPF) is a subset [...]