Wenbin Li (PhD Student)

Understanding physical phenomena is a key competence that enables humans and
animals to act and interact under uncertain perception in previously unseen
environments containing novel objects and their configurations. In this work,
we consider the problem of autonomous block stacking and explore solutions to
learning manipulation under physics constraints with visual perception inherent
to the task. Inspired by the intuitive physics in humans, we first present an
end-to-end learning-based approach to predict stability directly from
appearance, contrasting a more traditional model-based approach with explicit
3D representations and physical simulation. We study the model's behavior
together with an accompanied human subject test. It is then integrated into a
real-world robotic system to guide the placement of a single wood block into
the scene without collapsing existing tower structure. To further automate the
process of consecutive blocks stacking, we present an alternative approach
where the model learns the physics constraint through the interaction with the
environment, bypassing the dedicated physics learning as in the former part of
this work. In particular, we are interested in the type of tasks that require
the agent to reach a given goal state that may be different for every new
trial. Thereby we propose a deep reinforcement learning framework that learns
policies for stacking tasks which are parametrized by a target structure.

From autonomous driving cars to surgical robots, robotic system has enjoyed significant growth over the past decade. With the rapid development in robotics alongside the evolution in the related fields, such as computer vision and machine learning, integrating perception, anticipation and manipulation is key to the success of future robotic system. In this thesis, we explore different ways of such integration to extend the capabilities of a robotic system to take on more challenging real world tasks. On anticipation and perception, we address the recognition of ongoing activity from videos. In particular we focus on long-duration and complex activities and hence propose a new challenging dataset to facilitate the work. We introduce hierarchical labels over the activity classes and investigate the temporal accuracy-specificity trade-offs. We propose a new method based on recurrent neural networks that learns to predict over this hierarchy and realize accuracy specificity trade-offs. Our method outperforms several baselines on this new challenge. On manipulation with perception, we propose an efficient framework for programming a robot to use human tools. We first present a novel and compact model for using tools described by a tip model. Then we explore a strategy of utilizing a dual-gripper approach for manipulating tools – motivated by the absence of dexterous hands on widely available general purpose robots. Afterwards, we embed the tool use learning into a hierarchical architecture and evaluate it on a Baxter research robot. Finally, combining perception, anticipation and manipulation, we focus on a block stacking task. First we explore how to guide robot to place a single block into the scene without collapsing the existing structure. We introduce a mechanism to predict physical stability directly from visual input and evaluate it first on a synthetic data and then on real-world block stacking. Further, we introduce the target stacking task where the agent stacks blocks to reproduce a tower shown in an image. To do so, we create a synthetic block stacking environment with physics simulation in which the agent can learn block stacking end-to-end through trial and error, bypassing to explicitly model the corresponding physics knowledge. We propose a goal-parametrized GDQN model to plan with respect to the specific goal. We validate the model on both a navigation task in a classic gridworld environment and the block stacking task.

Understanding physical phenomena is a key component of human intelligence and

enables physical interaction with previously unseen environments. In this

paper, we study how an artificial agent can autonomously acquire this intuition

through interaction with the environment. We created a synthetic block stacking

environment with physics simulation in which the agent can learn a policy

end-to-end through trial and error. Thereby, we bypass to explicitly model

physical knowledge within the policy. We are specifically interested in tasks

that require the agent to reach a given goal state that may be different for

every new trial. To this end, we propose a deep reinforcement learning

framework that learns policies which are parametrized by a goal. We validated

the model on a toy example navigating in a grid world with different target

positions and in a block stacking task with different target structures of the

final tower. In contrast to prior work, our policies show better generalization

across different goals.

Understanding physical phenomena is a key competence that enables humans and

animals to act and interact under uncertain perception in previously unseen

environments containing novel object and their configurations. Developmental

psychology has shown that such skills are acquired by infants from observations

at a very early stage.

In this paper, we contrast a more traditional approach of taking a

model-based route with explicit 3D representations and physical simulation by

an end-to-end approach that directly predicts stability and related quantities

from appearance. We ask the question if and to what extent and quality such a

skill can directly be acquired in a data-driven way bypassing the need for an

explicit simulation.

We present a learning-based approach based on simulated data that predicts

stability of towers comprised of wooden blocks under different conditions and

quantities related to the potential fall of the towers. The evaluation is

carried out on synthetic data and compared to human judgments on the same

stimuli.

The recent progress in sparse coding and deep learning has made unsupervised

feature learning methods a strong competitor to hand-crafted descriptors. In

computer vision, success stories of learned features have been predominantly

reported for object recognition tasks. In this paper, we investigate if and how

feature learning can be used for material recognition. We propose two

strategies to incorporate scale information into the learning procedure

resulting in a novel multi-scale coding procedure. Our results show that our

learned features for material recognition outperform hand-crafted descriptors

on the FMD and the KTH-TIPS2 material classification benchmarks.