12 research outputs found
Social Attention: Modeling Attention in Human Crowds
Robots that navigate through human crowds need to be able to plan safe,
efficient, and human predictable trajectories. This is a particularly
challenging problem as it requires the robot to predict future human
trajectories within a crowd where everyone implicitly cooperates with each
other to avoid collisions. Previous approaches to human trajectory prediction
have modeled the interactions between humans as a function of proximity.
However, that is not necessarily true as some people in our immediate vicinity
moving in the same direction might not be as important as other people that are
further away, but that might collide with us in the future. In this work, we
propose Social Attention, a novel trajectory prediction model that captures the
relative importance of each person when navigating in the crowd, irrespective
of their proximity. We demonstrate the performance of our method against a
state-of-the-art approach on two publicly available crowd datasets and analyze
the trained attention model to gain a better understanding of which surrounding
agents humans attend to, when navigating in a crowd
Modeling Cooperative Navigation in Dense Human Crowds
For robots to be a part of our daily life, they need to be able to navigate
among crowds not only safely but also in a socially compliant fashion. This is
a challenging problem because humans tend to navigate by implicitly cooperating
with one another to avoid collisions, while heading toward their respective
destinations. Previous approaches have used hand-crafted functions based on
proximity to model human-human and human-robot interactions. However, these
approaches can only model simple interactions and fail to generalize for
complex crowded settings. In this paper, we develop an approach that models the
joint distribution over future trajectories of all interacting agents in the
crowd, through a local interaction model that we train using real human
trajectory data. The interaction model infers the velocity of each agent based
on the spatial orientation of other agents in his vicinity. During prediction,
our approach infers the goal of the agent from its past trajectory and uses the
learned model to predict its future trajectory. We demonstrate the performance
of our method against a state-of-the-art approach on a public dataset and show
that our model outperforms when predicting future trajectories for longer
horizons.Comment: Accepted at ICRA 201
CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models
Given access to accurate dynamical models, modern planning approaches are
effective in computing feasible and optimal plans for repetitive robotic tasks.
However, it is difficult to model the true dynamics of the real world before
execution, especially for tasks requiring interactions with objects whose
parameters are unknown. A recent planning approach, CMAX, tackles this problem
by adapting the planner online during execution to bias the resulting plans
away from inaccurately modeled regions. CMAX, while being provably guaranteed
to reach the goal, requires strong assumptions on the accuracy of the model
used for planning and fails to improve the quality of the solution over
repetitions of the same task. In this paper we propose CMAX++, an approach that
leverages real-world experience to improve the quality of resulting plans over
successive repetitions of a robotic task. CMAX++ achieves this by integrating
model-free learning using acquired experience with model-based planning using
the potentially inaccurate model. We provide provable guarantees on the
completeness and asymptotic convergence of CMAX++ to the optimal path cost as
the number of repetitions increases. CMAX++ is also shown to outperform
baselines in simulated robotic tasks including 3D mobile robot navigation where
the track friction is incorrectly modeled, and a 7D pick-and-place task where
the mass of the object is unknown leading to discrepancy between true and
modeled dynamics
Path Planning in Dynamic Environments with Adaptive Dimensionality
Path planning in the presence of dynamic obstacles is a challenging problem due to the added time dimension in search space. In approaches that ignore the time dimension and treat dynamic obstacles as static, frequent re-planning is unavoidable as the obstacles move, and their solutions are generally sub-optimal and can be incomplete. To achieve both optimality and completeness, it is necessary to consider the time dimension during planning. The notion of adaptive dimensionality has been successfully used in high-dimensional motion planning such as manipulation of robot arms, but has not been used in the context of path planning in dynamic environments. In this paper, we apply the idea of adaptive dimensionality to speed up path planning in dynamic environments for a robot with no assumptions on its dynamic model. Specifically, our approach considers the time dimension only in those regions of the environment where a potential collision may occur, and plans in a low-dimensional state-space elsewhere. We show that our approach is complete and is guaranteed to find a solution, if one exists, within a cost sub-optimality bound. We experimentally validate our method on the problem of 3D vehicle navigation (x, y, heading) in dynamic environments. Our results show that the presented approach achieves substantial speedups in planning time over 4D heuristic-based A*, especially when the resulting plan deviates significantly from the one suggested by the heuristic