Third-Party Effects

Most theories about effects of social embeddedness on trust define mechanisms that assume someone’s decision to trust is based on the reputation of the person to be trusted or on other available information. However, there is little empirical evidence about how subjects use the information that is available to them. In this chapter, we derive hypotheses about the effects of reputation and other information on trust from a range of theories and we devise an experiment that allows for testing these hypotheses simultaneously. We focus on the following mechanisms: learning, imitation, social comparison, and control. The results show that actors learn particularly from their own past experiences. Considering third-party information, imitation seems to be especially important

Third-Party Effects

Most theories about effects of social embeddedness on trust define mechanisms that assume someone’s decision to trust is based on the reputation of the person to be trusted or on other available information. However, there is little empirical evidence about how subjects use the information that is available to them. In this chapter, we derive hypotheses about the effects of reputation and other information on trust from a range of theories and we devise an experiment that allows for testing these hypotheses simultaneously. We focus on the following mechanisms: learning, imitation, social comparison, and control. The results show that actors learn particularly from their own past experiences. Considering third-party information, imitation seems to be especially important

Extraneousness-Aware Imitation Learning

Visual imitation learning provides an effective framework to learn skills from demonstrations. However, the quality of the provided demonstrations usually significantly affects the ability of an agent to acquire desired skills. Therefore, the standard visual imitation learning assumes near-optimal demonstrations, which are expensive or sometimes prohibitive to collect. Previous works propose to learn from noisy demonstrations; however, the noise is usually assumed to follow a context-independent distribution such as a uniform or gaussian distribution. In this paper, we consider another crucial yet underexplored setting -- imitation learning with task-irrelevant yet locally consistent segments in the demonstrations (e.g., wiping sweat while cutting potatoes in a cooking tutorial). We argue that such noise is common in real world data and term them "extraneous" segments. To tackle this problem, we introduce Extraneousness-Aware Imitation Learning (EIL), a self-supervised approach that learns visuomotor policies from third-person demonstrations with extraneous subsequences. EIL learns action-conditioned observation embeddings in a self-supervised manner and retrieves task-relevant observations across visual demonstrations while excluding the extraneous ones. Experimental results show that EIL outperforms strong baselines and achieves comparable policies to those trained with perfect demonstration on both simulated and real-world robot control tasks. The project page can be found at https://sites.google.com/view/eil-website.Comment: 7 pages, 6 figure

Time-Contrastive Networks: Self-Supervised Learning from Video

We propose a self-supervised approach for learning representations and robotic behaviors entirely from unlabeled videos recorded from multiple viewpoints, and study how this representation can be used in two robotic imitation settings: imitating object interactions from videos of humans, and imitating human poses. Imitation of human behavior requires a viewpoint-invariant representation that captures the relationships between end-effectors (hands or robot grippers) and the environment, object attributes, and body pose. We train our representations using a metric learning loss, where multiple simultaneous viewpoints of the same observation are attracted in the embedding space, while being repelled from temporal neighbors which are often visually similar but functionally different. In other words, the model simultaneously learns to recognize what is common between different-looking images, and what is different between similar-looking images. This signal causes our model to discover attributes that do not change across viewpoint, but do change across time, while ignoring nuisance variables such as occlusions, motion blur, lighting and background. We demonstrate that this representation can be used by a robot to directly mimic human poses without an explicit correspondence, and that it can be used as a reward function within a reinforcement learning algorithm. While representations are learned from an unlabeled collection of task-related videos, robot behaviors such as pouring are learned by watching a single 3rd-person demonstration by a human. Reward functions obtained by following the human demonstrations under the learned representation enable efficient reinforcement learning that is practical for real-world robotic systems. Video results, open-source code and dataset are available at https://sermanet.github.io/imitat

Human-to-Robot Imitation in the Wild

We approach the problem of learning by watching humans in the wild. While traditional approaches in Imitation and Reinforcement Learning are promising for learning in the real world, they are either sample inefficient or are constrained to lab settings. Meanwhile, there has been a lot of success in processing passive, unstructured human data. We propose tackling this problem via an efficient one-shot robot learning algorithm, centered around learning from a third-person perspective. We call our method WHIRL: In-the-Wild Human Imitating Robot Learning. WHIRL extracts a prior over the intent of the human demonstrator, using it to initialize our agent's policy. We introduce an efficient real-world policy learning scheme that improves using interactions. Our key contributions are a simple sampling-based policy optimization approach, a novel objective function for aligning human and robot videos as well as an exploration method to boost sample efficiency. We show one-shot generalization and success in real-world settings, including 20 different manipulation tasks in the wild. Videos and talk at https://human2robot.github.ioComment: Published at RSS 2022. Demos at https://human2robot.github.i