Publications

A Study of Human-Robot Handover through Human-Human Object Transfer

Charlotte Morissette

Bobak H. Baghi

Francois Hogan

In this preliminary study, we investigate changes in handover behaviour when transferring hazardous objects with the help of a high-resoluti… (see more)on touch sensor. Participants were asked to hand over a safe and hazardous object (a full cup and an empty cup) while instrumented with a modified STS sensor. Our data shows a clear distinction in the length of handover for the full cup vs the empty one, with the former being slower. Sensor data further suggests a change in tactile behaviour dependent on the object's risk factor. The results of this paper motivate a deeper study of tactile factors which could characterize a risky handover, allowing for safer human-robot interactions in the future.

2023-11-21

ArXiv (preprint)

doi.org

arxiv.org

Challenging Common Assumptions about Catastrophic Forgetting and Knowledge Accumulation

Timothee LESORT

Pau Rodriguez

Md Rifat Arefin

2023-11-20

Proceedings of The 2nd Conference on Lifelong Learning Agents (published)

proceedings.mlr.press

openreview.net

Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning

Hadi Nekoei

Akilesh Badrinaaraayanan

Amit Sinha

Mohammad Amin Amini

Janarthanan Rajendran

Aditya Mahajan

Sarath Chandar

2023-11-20

Proceedings of The 2nd Conference on Lifelong Learning Agents (published)

doi.org

arxiv.org

An Empirical Study of Self-Admitted Technical Debt in Machine Learning Software

Aaditya Bhatia

Foutse Khomh

Bram Adams

Ahmed E. Hassan

The emergence of open-source ML libraries such as TensorFlow and Google Auto ML has enabled developers to harness state-of-the-art ML algori… (see more)thms with minimal overhead. However, during this accelerated ML development process, said developers may often make sub-optimal design and implementation decisions, leading to the introduction of technical debt that, if not addressed promptly, can have a significant impact on the quality of the ML-based software. Developers frequently acknowledge these sub-optimal design and development choices through code comments during software development. These comments, which often highlight areas requiring additional work or refinement in the future, are known as self-admitted technical debt (SATD). This paper aims to investigate SATD in ML code by analyzing 318 open-source ML projects across five domains, along with 318 non-ML projects. We detected SATD in source code comments throughout the different project snapshots, conducted a manual analysis of the identified SATD sample to comprehend the nature of technical debt in the ML code, and performed a survival analysis of the SATD to understand the evolution of such debts. We observed: i) Machine learning projects have a median percentage of SATD that is twice the median percentage of SATD in non-machine learning projects. ii) ML pipeline components for data preprocessing and model generation logic are more susceptible to debt than model validation and deployment components. iii) SATDs appear in ML projects earlier in the development process compared to non-ML projects. iv) Long-lasting SATDs are typically introduced during extensive code changes that span multiple files exhibiting low complexity.

2023-11-20

ArXiv (preprint)

doi.org

arxiv.org

Responsible AI Research Needs Impact Statements Too

Alexandra Olteanu

Michael Ekstrand

Carlos Castillo

Jina Suh

All types of research, development, and policy work can have unintended, adverse consequences - work in responsible artificial intelligence … (see more)(RAI), ethical AI, or ethics in AI is no exception.

2023-11-20

ArXiv (preprint)

doi.org

arxiv.org

Substituting Data Annotation with Balanced Neighbourhoods and Collective Loss in Multi-label Text Classification

Muberra Ozmen

Joseph Cotnareanu

Mark Coates

Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text, and has a wide range of application domains… (see more). Most existing approaches require an enormous amount of annotated data to learn a classifier and/or a set of well-defined constraints on the label space structure, such as hierarchical relations which may be complicated to provide as the number of labels increases. In this paper, we study the MLTC problem in annotation-free and scarce-annotation settings in which the magnitude of available supervision signals is linear to the number of labels. Our method follows three steps, (1) mapping input text into a set of preliminary label likelihoods by natural language inference using a pre-trained language model, (2) calculating a signed label dependency graph by label descriptions, and (3) updating the preliminary label likelihoods with message passing along the label dependency graph, driven with a collective loss function that injects the information of expected label frequency and average multi-label cardinality of predictions. The experiments show that the proposed framework achieves effective performance under low supervision settings with almost imperceptible computational and memory overheads added to the usage of pre-trained language model outperforming its initial performance by 70% in terms of example-based F1 score.

2023-11-20

Proceedings of The 2nd Conference on Lifelong Learning Agents (published)

proceedings.mlr.press

Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges

Massimo Caccia

Jonas Mueller

Taesup Kim

Laurent Charlin

Rasool Fakoor

2023-11-20

Proceedings of The 2nd Conference on Lifelong Learning Agents (published)

proceedings.mlr.press