Human-Robot Interaction

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17 Sep

Feedback System for Kinesthetic Robot Teaching

Robots have the potential to assist humans not only in industrial tasks but also in everyday life tasks. However, programming robots typically require advanced technical expertise, which limits access for novice users. Learning from demonstration addresses this challenge by allowing novice users to teach robots new skills through physical demonstrations rather than writing sophisticated code, thereby promoting the democratization of robotics. Prior research has shown that novice users often provide suboptimal demonstrations, which can reduce the robot ability to learn and execute the desired task effectively. To address this issue, we aim to design an augmented-reality based real-time feedback system that supports users during demonstrations, enabling an effective communication between the human demonstrator (teacher) and the robotic system (learner).

System Design and Evaluation

Initially, it is important to identify potential ways to visualise the desired information content, aiming to reduce cognitive load while maximising teaching efficiency during demonstrations. Based on insights from focus group study (N=9), we designed the feedback system using Unity Engine and then we conducted a between-subject user study (N=36) to evaluate the system.

We found that proposed real-time feedback system improves multiple aspects of human performance, including mental workload, demonstration completion time, and task completion rate, compared to the conventional offline feedback system that only shows what the robot has learned after several demonstrations rather than assisting users during the demonstrations process itself.

This paper has been submitted to the ACM Designing Interactive Systems (DIS) 2026 conference and is currently under review.

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20 Mar

Impact of Robot Degree of Redundancy on Learning from Demonstration

Abstract: Learning from Demonstration allows robots to acquire skills from human demonstrations, making them more accessible to a wider range of users. Among different approaches, kinesthetic teaching allows humans to manipulate the robot joints directly, making it effective method for demonstrating constrained tasks. However, robots with kinematic redundancy enable multiple joint configurations to achieve a desired task, which could influence human teaching performance. One one hand, it could make it easier, allowing more freedom to demonstrate the task, but on the other, it also increases the number of joints that needs to be manipulated, potentially affecting cognitive and physical load of the demonstrator. Therefore, it is crucial to investigate how the number of degrees of redundancy (DoR) impact human performance during kinesthetic demonstrations, and then how these demonstrations influence robot performance. We simulated high and low DoR by locking one of the robot joint on a 7-DoF Panda robotic arm. We conducted a within-subject user study (N = 24) with two conditions: unconstrained condition (high DoR) and constrained condition (low DoR). We used a motion capture system to capture participants physical interaction with the robot when demonstrating two tasks: button pressing and cuboid block insertion. The results show that the robot’s DoR significantly affects mental workload, demonstration time, number of failed attempts, and physical interaction with the robot. Likewise, joint constraints significantly influence robot performance, measured by task completion using the learned model. These findings highlight the importance of considering robot DoR during demonstrating constrained tasks, allowing novice users to provide effective demonstrations.

Study Overview

Figure 1: Study overview showing the robotic platform, motion-capture system, and experimental setup.

User Experiment Videos

Button Pressing Task
Cuboid Block Insertion Task

Key Takeaways

Providing novice users with higher robot redundancy (more DoF than strictly required) helps them identify suitable joint configurations within a larger feasible solution space.
The sequence of hand contact points contains valuable information that can be used to anticipate and prevent potential failures.

Citation

Muhammad Bilal, D. Antony Chacon, Nir Lipovetzky, Denny Oetomo, Wafa Johal,  “Investigating the Impact of Robot Degree of Redundancy on Learning from Demonstration,” In 2026 21st ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2026, doi: 10.1145/3757279.3785606.
[In Press]