A Backpack Full of Skills: Egocentric Video Understanding with Diverse Task Perspectives

• URL: http://arxiv.org/abs/2403.03037v1
• Date: Tue, 5 Mar 2024 15:18:02 GMT
• Title: A Backpack Full of Skills: Egocentric Video Understanding with Diverse Task Perspectives
• Authors: Simone Alberto Peirone, Francesca Pistilli, Antonio Alliegro, Giuseppe Averta
• Abstract summary: We seek for a unified approach to video understanding which combines shared temporal modelling of human actions with minimal overhead. We propose EgoPack, a solution that creates a collection of task perspectives that can be carried across downstream tasks and used as a potential source of additional insights. We demonstrate the effectiveness and efficiency of our approach on four Ego4D benchmarks, outperforming current state-of-the-art methods.
• Score: 5.515192437680944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human comprehension of a video stream is naturally broad: in a few instants, we are able to understand what is happening, the relevance and relationship of objects, and forecast what will follow in the near future, everything all at once. We believe that - to effectively transfer such an holistic perception to intelligent machines - an important role is played by learning to correlate concepts and to abstract knowledge coming from different tasks, to synergistically exploit them when learning novel skills. To accomplish this, we seek for a unified approach to video understanding which combines shared temporal modelling of human actions with minimal overhead, to support multiple downstream tasks and enable cooperation when learning novel skills. We then propose EgoPack, a solution that creates a collection of task perspectives that can be carried across downstream tasks and used as a potential source of additional insights, as a backpack of skills that a robot can carry around and use when needed. We demonstrate the effectiveness and efficiency of our approach on four Ego4D benchmarks, outperforming current state-of-the-art methods.

Related papers

• Self-Explainable Affordance Learning with Embodied Caption [63.88435741872204]
  We introduce Self-Explainable Affordance learning (SEA) with embodied caption. SEA enables robots to articulate their intentions and bridge the gap between explainable vision-language caption and visual affordance learning. We propose a novel model to effectively combine affordance grounding with self-explanation in a simple but efficient manner.
  arXiv  Detail & Related papers  (2024-04-08T15:22:38Z)
• Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning [49.92517970237088]
  We tackle the problem of training a robot to understand multimodal prompts. This type of task poses a major challenge to robots' capability to understand the interconnection and complementarity between vision and language signals. We introduce an effective framework that learns a policy to perform robot manipulation with multimodal prompts.
  arXiv  Detail & Related papers  (2023-10-14T22:24:58Z)
• Human-oriented Representation Learning for Robotic Manipulation [64.59499047836637]
  Humans inherently possess generalizable visual representations that empower them to efficiently explore and interact with the environments in manipulation tasks. We formalize this idea through the lens of human-oriented multi-task fine-tuning on top of pre-trained visual encoders. Our Task Fusion Decoder consistently improves the representation of three state-of-the-art visual encoders for downstream manipulation policy-learning.
  arXiv  Detail & Related papers  (2023-10-04T17:59:38Z)
• RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot [56.130215236125224]
  A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots. Recent research in one-shot imitation learning has shown promise in transferring trained policies to new tasks based on demonstrations. This paper aims to unlock the potential for an agent to generalize to hundreds of real-world skills with multi-modal perception.
  arXiv  Detail & Related papers  (2023-07-02T15:33:31Z)
• Identifying Auxiliary or Adversarial Tasks Using Necessary Condition Analysis for Adversarial Multi-task Video Understanding [34.75145779372538]
  We propose a generalized notion of multi-task learning by incorporating both auxiliary tasks that the model should perform well on and adversarial tasks that the model should not perform well on. Our novel proposed framework, Adversarial Multi-Task Neural Networks (AMT), penalizes adversarial tasks, determined by NCA to be scene recognition. We show that our approach improves accuracy by 3% and encourages the model to attend to action features instead of correlation-biasing scene features.
  arXiv  Detail & Related papers  (2022-08-22T06:26:11Z)
• Unsupervised Reinforcement Learning for Transferable Manipulation Skill Discovery [22.32327908453603]
  Current reinforcement learning (RL) in robotics often experiences difficulty in generalizing to new downstream tasks. We propose a framework that pre-trains the agent in a task-agnostic manner without access to the task-specific reward. We show that our approach achieves the most diverse interacting behavior and significantly improves sample efficiency in downstream tasks.
  arXiv  Detail & Related papers  (2022-04-29T06:57:46Z)
• MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale [103.7609761511652]
  We show how a large-scale collective robotic learning system can acquire a repertoire of behaviors simultaneously. New tasks can be continuously instantiated from previously learned tasks. We train and evaluate our system on a set of 12 real-world tasks with data collected from 7 robots.
  arXiv  Detail & Related papers  (2021-04-16T16:38:02Z)
• Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills [93.12417203541948]
  We propose the objective of learning a functional understanding of the environment by learning to reach any goal state in a given dataset. We find that our method can operate on high-dimensional camera images and learn a variety of skills on real robots that generalize to previously unseen scenes and objects.
  arXiv  Detail & Related papers  (2021-04-15T20:10:11Z)
• SKID RAW: Skill Discovery from Raw Trajectories [23.871402375721285]
  It is desirable to only demonstrate full task executions instead of all individual skills. We propose a novel approach that simultaneously learns to segment trajectories into reoccurring patterns. The approach learns a skill conditioning that can be used to understand possible sequences of skills.
  arXiv  Detail & Related papers  (2021-03-26T17:27:13Z)
• Learning Transferable Concepts in Deep Reinforcement Learning [0.7161783472741748]
  We show that learning discrete representations of sensory inputs can provide a high-level abstraction that is common across multiple tasks. In particular, we show that it is possible to learn such representations by self-supervision, following an information theoretic approach. Our method is able to learn concepts in locomotive and optimal control tasks that increase the sample efficiency in both known and unknown tasks.
  arXiv  Detail & Related papers  (2020-05-16T04:45:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.