Abstract
Home robots should swiftly adapt by learning user preferences, understanding environmental constraints, and mastering new tasks. These lifelong learning processes should involve user demonstrations or dialogues, allowing the robot to receive guidance and resolve ambiguities through interaction with the user. Despite significant advancements in both high-level and low-level behavioral systems for robotics, several challenges persist regarding their applicability in home environments. Existing systems are typically designed for specific tasks and often struggle to adjust to changes or learn from ongoing experiences, rendering them less effective in dynamic, real-world settings like homes. To address these challenges, it is crucial to develop robots capable of lifelong learning. Lifelong learning enables robots to build complex, nuanced representations of their surroundings, facilitating continuous adaptation and informed decision-making based on their current environment, allowing robots to transcend the limitations of their initial training and evolve through interactions and experiences.

Invited Speakers

Schedule

Time
8:50		Organizers Introductory Remarks
9:00		Keynote 1: Danica Kragic Tentative title Abstract ...
9:20		Keynote 2: Tamim Asfour Tentative title Abstract ...
9:40		Keynote 3: Lerrel Pinto Tentative title Abstract ...
10:00		Spotlight Talks
11:00		Keynote 4: Yonatan Bisk Tentative title Abstract ...
11:20		Coffee Break, Socializing, Posters
12:00		Keynote 5: Ted Xiao Tentative title Abstract ...
12:20		Panel Discussion Panelists: Danica Kragic, Tamim Asfour, Lerrel Pinto, Yonatan Bisk, Ted Xiao
12:50		Organizers Closing Remarks

Call for Papers

Submission Guidelines

CoRL LLHomeRobots 2024 suggests 4+N (short) or 8+N (full) paper length formats — i.e., 4 or 8 pages of main content with unlimited additional pages for references, appendices, etc.

Submissions will be handled through CMT: https://cmt3.research.microsoft.com/LLHOMEROBOTS2024

We will accept the official LaTeX paper template, provided by CoRL 2024.

Our review process will be double-blind, following the CoRL 2024 paper submission policy.

All accepted papers will be invited for poster presentations; the highest-rated papers, according to the Technical Program Committee, will be given spotlight presentations. Accepted papers will be made available online on this workshop website as non-archival reports, allowing authors to also submit their works to future conferences or journals. We will highlight the Best Reviewer and reveal the Best Paper Award during the closing remarks at the workshop event.

Key areas we are targeting in this workshop include:

• How can robots effectively learn from human demonstrations and feedback in a natural and intuitive manner?
  
• How can robots learn and adapt to the individual preferences and routines of different household members?
  
• What mechanisms are needed to ensure personalized and user-specific interactions and task performances?
  
• What representations can serve as robust and flexible memory systems that allow household robots to recall past experiences and apply this knowledge to new situations?
  
• What are the optimal strategies for balancing short-term (e.g., ‘working memory’) and long-term memory in robotic systems?
  
• How can robots autonomously detect and correct errors in task execution, with or without human intervention?
  
• What frameworks can enable robots to learn from their mistakes and improve over time?
  
• Few-shot learning or prompting for learning new behaviours
  
• Neural architectures of large models to support lifelong learning
  
• What metrics should we be focusing on?
  
• The role of reinforcement learning and LLMs in lifelong learning
  

Important Dates

• Submission deadline: 15 October 2024, 23:59 AOE.
• Author Notifications: 25 October 2024, 23:59 AOE.
• Camera Ready: 3 November 2024, 23:59 AOE.
• Workshop: 9 November 2024, 08:50-12:50 CET

Organizers