Student Talks
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 [...]
Foundation Control Model for General Embodied Intelligence
Abstract: With the growing accessibility of humanoid hardware and rapid advances in foundation models, we are entering an era where achieving general embodied intelligence is within reach—enabling humanoid robots to perform a wide range of tasks in human-centric environments. Despite significant progress in language and vision foundation models, controlling humanoids with high degrees of freedom [...]
Learning Humanoid Robots from Simulation to Real to Simulation
Abstract: How do we teach humanoid robots to move like humans—and do so reliably in the real world? In this talk, I’ll share my journey in building a learning-based pipeline that closes the loop between simulation and reality for humanoid whole-body control. Starting from real-time teleoperation (H2O), to scalable data humanoid collection (OmniH2O), to learning [...]
Experience-Based Action Advising for Multi-Agent Teaming
Abstract: We study how to improve coordination efficiency for multi-agent teams with heterogeneously experienced agents. In such a setting, experienced agents can transfer their knowledge to less experienced agents to accelerate their learning, while leveraging the students' initial expertise to inform what knowledge to transfer. Inspired by this idea, this work specifically assumes one teacher [...]
Towards Controllable Sampling and Diverse Score Distillation in Diffusion Models
Abstract: Denoising diffusion models have emerged as a powerful paradigm for generative modeling, which has been widely used for perception, generation, and action. These models can be utilized through sampling or score distillation; however, existing methods lack controllability in sampling and suffer from limited diversity in score distillation. In this thesis, we propose two complementary mechanisms to enhance the [...]
RESCUE Rollers: A Platform for Collaborative, Multi-robot Exploration in Search and Rescue
Abstract: The use of robotic platforms for search and rescue remains a significant challenge for many roboticist. While human and animal first responders play critical roles, their effectiveness can be limited by biological constraints. Robotic systems offer the potential to overcome these limitations, especially in environments inaccessible to humans and animals due to size or [...]
Scaling, Automating and Adapting Sim-to-real Policy Learning
Abstract: Building a generalist robot capable of performing diverse tasks in unstructured environments remains a longstanding challenge. A recent trend in robot learning aims to address this by scaling up demonstration datasets for imitation learning. However, most large-scale robotics datasets are collected in the real-world, often via manual teleoperation. This process is labor-intensive, slow, hardware-dependent, [...]
Generative 3D Garment Modeling with Sparse Visual Cues
Abstract: As digital apparel becomes increasingly vital to virtual environments and personalized experiences, there is a growing need for intuitive tools that enable non-experts to create and interact with 3D garments. To broaden accessibility, these tools must function effectively with minimal input - raising the key question: How can we achieve high-quality 3D garment modeling [...]