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Active Robot Curriculum Learning from Online Human Demonstrations
Robotics · Submitted 4 Mar 2025
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our results show that our method significantly improves learning performance compared to three other LfD baselines in terms of the final success rate of the converged policy and sample efficiency.
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The abstract, sentence by sentence
Learning from Demonstrations (LfD) allows robots to learn skills from human users, but its effectiveness can suffer due to sub-optimal teaching, especially from untrained demonstrators. Active LfD aims to improve this by letting robots actively request demonstrations to enhance learning. However, this may lead to frequent context switches between various task situations, increasing the human cognitive load and introducing errors to demonstrations. Moreover, few prior studies in active LfD have examined how these active query strategies may impact human teaching in aspects beyond user experience, which can be crucial for developing algorithms that benefit both robot learning and human teaching. To tackle these challenges, we propose an active LfD method that optimizes the query sequence of online human demonstrations via Curriculum Learning (CL), where demonstrators are guided to provide demonstrations in situations of gradually increasing difficulty. We evaluate our method across four simulated robotic tasks with sparse rewards and conduct a user study (N=26) to investigate the influence of active LfD methods on human teaching regarding teaching performance, post-guidance teaching adaptivity, and teaching transferability. Our results show that our method significantly improves learning performance compared to three other LfD baselines in terms of the final success rate of the converged policy and sample efficiency. Additionally, results from our user study indicate that our method significantly reduces the time required from human demonstrators and decreases failed demonstration attempts. It also enhances post-guidance human teaching in both seen and unseen scenarios compared to another active LfD baseline, indicating enhanced teaching performance, greater post-guidance teaching adaptivity, and better teaching transferability achieved by our method.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Learning from Demonstrations (LfD) allows robots to learn skills from human users, but its effectiveness can suffer due to sub-optimal teaching, especially from untrained demonstrators. | background 1.00 | 0.04 | 0.02 | asserted 0.50 |
| s1 | Active LfD aims to improve this by letting robots actively request demonstrations to enhance learning. | background 1.00 | 0.04 | 0.02 | asserted 0.95 |
| s2 | However, this may lead to frequent context switches between various task situations, increasing the human cognitive load and introducing errors to demonstrations. | background 0.90 | 0.06 | 0.02 | hedged possibility 0.99 |
| s3 | Moreover, few prior studies in active LfD have examined how these active query strategies may impact human teaching in aspects beyond user experience, which can be crucial for developing algorithms that benefit both robot learning and human teaching. | gap 1.00 | 0.04 | 0.02 | asserted 0.72 |
| s4 | To tackle these challenges, we propose an active LfD method that optimizes the query sequence of online human demonstrations via Curriculum Learning (CL), where demonstrators are guided to provide demonstrations in situations of gradually increasing difficulty. | method 0.66 | 0.62 | 0.05 | asserted 0.99 |
| s5 | We evaluate our method across four simulated robotic tasks with sparse rewards and conduct a user study (N=26) to investigate the influence of active LfD methods on human teaching regarding teaching performance, post-guidance teaching adaptivity, and teaching transferability. | method 0.80 | 0.97 | 0.04 | asserted 0.82 |
| s6 | Our results show that our method significantly improves learning performance compared to three other LfD baselines in terms of the final success rate of the converged policy and sample efficiency. | result 1.00 | 0.97 | 0.38 | asserted 1.00 |
| s7 | Additionally, results from our user study indicate that our method significantly reduces the time required from human demonstrators and decreases failed demonstration attempts. | result 1.00 | 0.96 | 0.15 | asserted 1.00 |
| s8 | It also enhances post-guidance human teaching in both seen and unseen scenarios compared to another active LfD baseline, indicating enhanced teaching performance, greater post-guidance teaching adaptivity, and better teaching transferability achieved by our method. | result 0.65 | 0.96 | 0.37 | asserted 0.55 |
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The paper
Original abstract
Learning from Demonstrations (LfD) allows robots to learn skills from human users, but its effectiveness can suffer due to sub-optimal teaching, especially from untrained demonstrators. Active LfD aims to improve this by letting robots actively request demonstrations to enhance learning. However, this may lead to frequent context switches between various task situations, increasing the human cognitive load and introducing errors to demonstrations. Moreover, few prior studies in active LfD have examined how these active query strategies may impact human teaching in aspects beyond user experience, which can be crucial for developing algorithms that benefit both robot learning and human teaching. To tackle these challenges, we propose an active LfD method that optimizes the query sequence of online human demonstrations via Curriculum Learning (CL), where demonstrators are guided to provide demonstrations in situations of gradually increasing difficulty. We evaluate our method across four simulated robotic tasks with sparse rewards and conduct a user study (N=26) to investigate the influence of active LfD methods on human teaching regarding teaching performance, post-guidance teaching adaptivity, and teaching transferability. Our results show that our method significantly improves learning performance compared to three other LfD baselines in terms of the final success rate of the converged policy and sample efficiency. Additionally, results from our user study indicate that our method significantly reduces the time required from human demonstrators and decreases failed demonstration attempts. It also enhances post-guidance human teaching in both seen and unseen scenarios compared to another active LfD baseline, indicating enhanced teaching performance, greater post-guidance teaching adaptivity, and better teaching transferability achieved by our method.
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References
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What it cites · 4 sentences without a confident label
Uses the method or tool of 1 paper
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In this work, we utilize Advantage Weighted Actor-Critic (AWAC) [ref] , an advanced LfD algorithm, to update the policy with human demonstrations.
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets · Background on Advantage Weighted Actor-Critic · 0.97
Compares with 1 paper
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While [ref] also aims to influence human teachers to select demonstrations that better support robot learning, their approach relies on visual feedback from AR devices and has only been tested on simple 2D navigation tasks.
How Can Everyday Users Efficiently Teach Robots by Demonstrations? · Active Learning from Demonstrations · 0.90
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