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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.

Amphibious robots must move across environments where fluid forces, body contact, and solid structures interact in complex and often unpredictable ways. These interactions are difficult to model accurately, making adaptive and sample-efficient control especially important.

In this project, we will explore how online optimization can improve CPG-based amphibious locomotion controllers. Using onboard sensors such as contact, flow, and proprioceptive sensing, the robot will tune its controller to achieve more agile, efficient, and robust multimodal locomotion. Depending on the project scope, the work may focus on sample-efficient optimization in simulation and simulation-to-robot transfer.

This project is suitable for students who have experience in optimization and MuJoCo simulations. Experience in CPG networks, system identification, signal processing, Arduino/Raspberry Pi programming, and ROS 2 can be very 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 for 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 autonomously reorganize room layouts, clear pathways, transport objects, and grasp and deliver everyday items to users.

This master’s thesis aims to integrate mobile robotic furniture, an origami-inspired robotic arm, markerless perception, and language-based interfaces into a complete assistive mobile manipulation system.

Current Progress

1. Mobile robotic furniture: We have developed a group of mobile robotic furniture units that can autonomously navigate and reconfigure indoor environments.
   References: Publication 1, Publication 2


2. Multi-robot dense coordination: Our Velocity Potential Field Modulation (VPFM) algorithm guides multiple robotic furniture units toward their target poses while avoiding collisions and deadlocks.
   Reference: VPFM publication


3. Markerless tracking and body-language interaction: A multi-view RGB-D system tracks users and furniture units in real time. It provides visual feedback for multi-robot coordination and enables users to control robotic furniture through simple hand motions. The single-view version has been published, while the multi-view version is currently under review.
   References: Single-view system, AI for Good Global Summit 2026 demonstration


4. LLM/VLM supported language interfaces: A natural-language interface supported by a large language model and a vision-language model allows the user to request tasks such as “Bring me a cup of water.” The system can interpret the request, localize the desired object, and identify the furniture on which it is placed. The related paper is under preparation, and a demonstration can be presented during the interview.


5. Origami-inspired robotic arm: The project uses an origami-inspired arm developed by the EPFL Reconfigurable Robotics Lab. The arm can remain folded when not in use and extend for grasping tasks, making it suitable for integration with robotic furniture.
   Reference: Origami-inspired third arm

Thesis Objectives

1. Mechanical and electronic integration: Integrate the origami-inspired arm with a mobile robotic furniture platform. An initial configuration is available, but the student will be encouraged to improve the design to increase the workspace, reachability, and stability of the mobile manipulator.


2. Whole-body control: Develop a controller that coordinates the mobile base and robotic arm. The controller will use feedback from the markerless tracking system and the VLM-based object-grounding module to generate commands for positioning, navigation, grasping, and collision avoidance.


3. End-to-end demonstration: Implement the following scenario:
   • A wheelchair user says, “Bring me a cup of water.”
   • The system interprets the request and localizes the target cup on a static piece of furniture.
   • The system may ask the user for confirmation when the request is ambiguous.
   • A mobile table equipped with the origami arm navigates to the object and positions itself for manipulation.
   • The arm grasps the cup, and the mobile table brings it back to the user.

The final demonstration will integrate speech interaction, visual grounding, markerless tracking, mobile navigation, whole-body control, and robotic grasping.

Desired Candidate Profile

• Experience with ROS 2, C++, and Python;
• Experience with mechanical design or rapid prototyping;
• Interest in mobile manipulation, assistive robotics, robot perception, or human–robot interaction;
• Experience developing real-world robotic applications is an advantage.

This master thesis will be co-supervised by BioRob and RRL

• Contact in BioRob: Lixuan Tang (lixuan.tang@epfl.ch)
• Contact in RRL: Alexander Schuessler (alexander.schuessler@epfl.ch)

This project has been taken for 2026 fall semester

Background

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

Coordinating multiple robotic furniture units in confined indoor environments is challenging because their physical shapes and dimensions cannot be neglected. Unlike point robots, furniture units must account for their full geometry when navigating through narrow spaces and interacting with one another.

Current Progress

Our previously developed algorithm, Velocity Potential Field Modulation (VPFM), has demonstrated strong performance in the dense coordination of polytopic robot swarms in 2D environments. Given the current pose and velocity of each robot, together with its target pose, VPFM generates collision-aware motion commands that guide all robots toward their assigned targets while reducing the risk of collisions and deadlocks.

More information is available in our IEEE Robotics and Automation Letters publication and the corresponding open-source repository:

Paper: IEEE RA-L publication

Project Objectives

1. Adapting VPFM to an indoor maze map with static obstacles.
   An ordinary home environment consists of multiple rooms separated by walls and doors, as well as static furniture pieces. To ensure that each mobile robotic furniture unit can arrive at any reachable position in the home environment, combining VPFM with a global path planner can help the swarm of mobile robotic furniture to overcome bottleneck regions and resolve the deadlock.


2. Conduct simulation-based validation.
   Implement and evaluate the proposed methods in simulation using representative dense indoor scenarios. The evaluation should investigate collision avoidance, deadlock resolution, convergence to target poses, computational efficiency, and the benefits of combining a global path planner with the local VPFM coordinator.

This project has been taken for 2026 fall semester

Background

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

Coordinating multiple robotic furniture units in confined indoor environments is challenging because their physical shapes and dimensions cannot be neglected. Unlike point robots, furniture units must account for their full geometry when navigating through narrow spaces and interacting with one another.

Current Progress

Our previously developed algorithm, Velocity Potential Field Modulation (VPFM), has demonstrated strong performance in the dense coordination of polytopic robot swarms in 2D environments. Given the current pose and velocity of each robot, together with its target pose, VPFM generates collision-aware motion commands that guide all robots toward their assigned targets while reducing the risk of collisions and deadlocks.

More information is available in our IEEE Robotics and Automation Letters publication and the corresponding open-source repository:

Paper: IEEE RA-L publication

Project Objectives

1. Extend VPFM to height-aware, multi-layer coordination.
   Develop a representation and coordination framework that accounts for the three-dimensional geometry of robotic furniture while maintaining predominantly planar motion. This extension should allow compatible parts of different furniture units to overlap vertically. For example, a low table may move underneath a higher table, and the seat of a chair may pass below a tabletop, while the chair back must still be treated as a potential collision.


2. Conduct simulation-based validation.
   Implement and evaluate the proposed methods in simulation using representative dense indoor scenarios. The evaluation should investigate collision avoidance, deadlock resolution, convergence to target poses, computational efficiency, and the benefits of height-aware coordination compared with the original two-dimensional formulation.