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This project has been taken

Machine learning has shown great potential for enabling robots to acquire robust and adaptive locomotion skills. For hyper-redundant robots, such as salamander-inspired robots, this remains challenging because of the high-dimensional body dynamics, the diversity of possible behaviors, and the need to integrate multimodal sensory feedback from both terrestrial and aquatic environments.

This project is an extension of a previous project, in which we will keep exploring learning-based methods for improving salamander robot locomotion in complex amphibious environments. Possible directions include sensor fusion, maneuvering, transition, sim-real transfer, etc. A key goal will be to evaluate whether biological/physical priors can improve learning efficiency, robustness, and smooth transitions between behaviors.

The project is suitable for a highly self-motivated student with complete project experience in machine learning. Familiarity with MuJoCo MJX or other physics simulators is expected. Experience with CPGs, signal processing, reinforcement learning, or sensor-based control will be helpful.

Students who are interested in this project shall send the following materials to the assistant: (1) resume, (2) transcript showing relevant courses and grades, and (3) other materials that can demonstrate their skills and project experience (such as videos, slides, Git repositories, etc.).

This project has been taken

Animals display a rich diversity of behaviors in natural environments, but only a small set of them have been well studied and simplified into template gaits. Recent advances in imitation learning have enabled quadruped and humanoid robots to reproduce complex animal and human motions, but these approaches have been less explored for amphibious robots that must coordinate body, limbs, and environmental interactions across water and land.

In this project, we will work on the recordings of real salamanders in a vivarium with various terrains. We will extract multi-terrain locomotion behaviors from the collected motion data. Then we will investigate how diverse salamander behaviors can be transferred to simplified salamander models or salamander-inspired robots. Note that because we rely on DeepLabCut to track the animal kinematics from videos, the student may spend half a semester on labeling video data , unless a better tracking method is found or developed.

The student is expected to be familiar with Python programming, signal processing, kinematics, and imitation learning. Experience with dimensionality reduction, MuJoCo simulation, and CPGs is highly recommended.

Students who are interested in this project shall send the following materials to the assistants: (1) resume, (2) transcript showing relevant courses and grades, and (3) other materials that can demonstrate their skills and project experience (such as videos, slides, Git repositories, etc.).

This project has been taken for the 2026 Fall semester.

Background

To improve the indoor autonomy of people with mobility impairments, including wheelchair users, everyday furniture can be augmented with mobility and manipulation capabilities. Such robotic furniture could reorganize room layouts, clear pathways, transport objects, and deliver everyday items to users.

This semester project focuses on the user-centric design of such an assistive environment. The goal is to understand the everyday challenges, functional limitations, and interaction needs of wheelchair users, and to investigate how multiple user interfaces can provide accessible and intuitive control of robotic furniture.

Current Progress

1. Mobile robotic furniture: A group of robotic furniture units capable of autonomous navigation and room reconfiguration.
   References: Publication 1, Publication 2


2. Markerless body-language interaction: A multi-view RGB-D system tracks users and furniture in real time and enables simple gesture-based interaction.
   References: AI for Good 2026 demonstration


3. LLM/VLM-supported natural-language interface: Users can give commands such as “Bring me a cup of water,” while the system interprets the request and identifies relevant objects and furniture.


4. Origami-inspired robotic arm: A compact foldable robotic arm can be integrated with robotic furniture or the wheelchair for grasping and delivery tasks.
   Reference: Origami-inspired third arm


5. Tablet interface: A bird's-eye visualization allows users to select predefined layouts or reposition furniture through drag-and-drop interaction.

Project Objectives

1. User-Centric Study: Investigate the everyday challenges faced by wheelchair users through literature review and existing user surveys. Understand how different mobility impairments affect functional abilities and interaction needs, identify meaningful assistive applications, and define requirements for future user studies.


2. Multimodal Interface Integration: Investigate how origami arm, gesture, tablet, natural language, and/or other interfaces can complement each other. The aim is to ensure that users with different abilities or under different conditions always have an accessible way to trigger, modify, or stop an assistive function.


3. Interface Exploration: Depending on the student's interests, further develop or evaluate one specific interaction modality.

Desired Candidate Profile

• Strong interest in user-centric research, assistive technology, rehabilitation, or human–robot interaction;
• Interest in understanding how mobility impairments affect functional abilities and interaction with technology;
• Background in neuroscience, neuroengineering, biomedical engineering, rehabilitation, robotics, HCI, or related fields is welcome;
• Experience with user studies, experimental design, rapid prototyping, or robotic systems is an advantage.