  Real-world applications require a robot operating in the physical world with
awareness of potential risks besides accomplishing the task. A large part of
risky behaviors arises from interacting with objects in ignorance of
affordance. To prevent the agent from making unsafe decisions, we propose to
train a robotic agent by reinforcement learning to execute tasks with an
awareness of physical properties such as mass and friction in an indoor
environment. We achieve this through a novel physics-inspired reward function
that encourages the agent to learn a policy discerning different masses and
friction coefficients. We introduce two novel and challenging indoor
rearrangement tasks -- the variable friction pushing task and the variable mass
pushing task -- that allow evaluation of the learned policies in trading off
performance and physics-inspired risk. Our results demonstrate that by
equipping with the proposed reward, the agent is able to learn policies
choosing the pushing targets or goal-reaching trajectories with minimum
physical cost, which can be further utilized as a precaution to constrain the
agent's behavior in a safety-critic environment.
