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Title: Deep Imitative Models for Flexible Inference, Planning, and Control

Paper content:
Imitation learning provides an appealing framework for autonomous control: in many tasks, demonstrations of preferred behavior can be readily obtained from human experts, removing the need for costly and potentially dangerous online data collection in the real world.
However, policies learned with imitation learning have limited flexibility to accommodate varied goals at test time.
Model-based reinforcement learning (MBRL) offers considerably more flexibility, since a predictive model learned from data can be used to achieve various goals at test time.
However, MBRL suffers from two shortcomings.
First, the model does not help to choose desired or safe outcomes -- its dynamics estimate only what is possible, not what is preferred.
Second, MBRL typically requires additional online data collection to ensure that the model is accurate in those situations that are actually encountered when attempting to achieve test time goals.
Collecting this data with a partially trained model can be dangerous and time-consuming.
In this paper, we aim to combine the benefits of imitation learning and MBRL, and propose imitative models: probabilistic predictive models able to plan expert-like trajectories to achieve arbitrary goals.
We find this method substantially outperforms both direct imitation and MBRL in a simulated autonomous driving task, and can be learned efficiently from a fixed set of expert demonstrations without additional online data collection.
We also show our model can flexibly incorporate user-supplied costs at test-time, can plan to sequences of goals, and can even perform well with imprecise goals, including goals on the wrong side of the road.
Reinforcement learning (RL) algorithms offer the promise of automatically learning behaviors from raw sensory inputs with minimal engineering.
However, RL generally requires online learning: the agent must collect more data with its latest strategy, use this data to update a model, and repeat.
While this is natural in some settings, deploying a partially-trained policy on a real-world autonomous system, such as a car or robot, can be dangerous.
In these settings the behavior must be learned offline, usually with expert demonstrations.
How can we incorporate such demonstrations into a flexible robotic system, like an autonomous car?
One option is imitation learning (IL), which can learn policies that stay near the expert's distribution.
Another option is model-based RL (MBRL) BID8 BID2 , which can use the data to fit a dynamics model, and can in principle be used with planning algorithms to achieve any user-specified goal at test time.
However, in practice, model-based and model-free RL algorithms are vulnerable to distributional drift BID32 BID24 : when acting according to the learned model or policy, the agent visits states different from those seen during training, and in those it is unlikely to determine an effective course of action.
This is especially problematic when the data intentionally excludes adverse events, such as crashes.
A model ignorant to the possibility of a crash cannot know how to prevent it.
Therefore, MBRL algorithms usually require online collection and training BID6 BID12 .
Imitation learning algorithms use expert demonstration data and, despite similar drift shortcomings BID26 , can sometimes learn effective policies without additional online data collection BID35 .
However, standard IL offers little task flexibility since it only predicts low-level behavior.
While several works augmented IL with goal conditioning BID4 BID1 , these goals must be specified in advance during training, and are typically simple (e.g., turning left or right).Figure
1: We apply our approach to navigation in CARLA BID5 . Columns
1,2: Images depicting the current scene. The overhead
image depicts a 50 m 2 area. Column 3: LIDAR
input and goals are provided to our deep imitative trajectory model, and plans to the goals are computed under the model's likelihood objective, and colored according to their ranking under the objective, with red indicating the best plan. The red square
indicates the chosen high-level goal, and the yellow cross indicates a point along our plan used as a setpoint for a PID controller. The LIDAR map
is 100 m 2 , and each goal is ≥20 m away from the vehicle. Column 4: Our
model can incorporate arbitrary test-time costs, and use them to adjust its planning objective and plan ranking.Figure 2: A brief taxonomy of learning-based control methods. In our scenario
, we avoid online data collection, specifically from the policy we seek to imitate. We structure our
imitation learner with a model to make it flexible to new tasks at test time. We compare against
other offline approaches (front face).The goal in our work
is to devise a new algorithm that combines the advantages of IL and MBRL, affording both the flexibility to achieve new user-specified goals at test time and the ability to learn entirely from offline data. By learning a deep probabilistic
predictive model from expert-provided data, we capture the distribution of expert behaviors without using manually designed reward functions. To plan to a goal, our method infers
the most probable expert state trajectory, conditioned on the current position and reaching the goal. By incorporating a model-based representation
, our method can easily plan to previously unseen user-specified goals while respecting rules of the road, and can be flexibly repurposed to perform a wide range of test-time tasks without any additional training. Inference with this model resembles trajectory
optimization in model-based reinforcement learning, and learning this model resembles imitation learning. Our method's relationship to other work is illustrated
in Fig. 2 . We demonstrate our method on a simulated autonomous driving
task (see FIG0 . A high-level route planner provides navigational goals, which
our model uses to automatically generate plans that obey the rules of the road, inferred entirely from data. In contrast to IL, our method produces an interpretable distribution
over trajectories and can follow a variety of goals without additional training. In contrast to MBRL, our method generates human-like behaviors without
additional data collection or learning. In our experiments, our approach substantially outperforms both MBRL and
IL: it can efficiently learn near-perfect driving through the static-world CARLA simulator from just 7,000 trajectories obtained from 19 hours of driving. We also show that our model can flexibly incorporate and achieve goals not
seen during training, and is robust to errors in the high-level navigation system, even when the high-level goals are on the wrong side of the road. Videos of our results are available.
We proposed a method that combines elements of imitation learning and model-based reinforcement learning (MBRL).
Our method first learns what preferred behavior is by fitting a probabilistic model to the distribution of expert demonstrations at training time, and then plans paths to achieve userspecified goals at test time while maintaining high probability under this distribution.
We demonstrated several advantages and applications of our algorithm in autonomous driving scenarios.
In the context of MBRL, our method mitigates the distributional drift issue by explicitly preferring plans that stay close to the expert demonstration data.
This implicitly allows our method to enforce basic safety properties: in contrast to MBRL, which requires negative examples to understand the potential for adverse outcomes (e.g., crashes), our method automatically avoids such outcomes specifically because they do not occur (or rarely occur) in the training data.
In the context of imitation learning, our method provides a flexible, safe way to generalize to new goals by planning, compared to prior work on black-box, model-free conditional imitation learning.
Our algorithm produces an explicit plan within the distribution of preferred behavior accompanied with a score: the former offers interpretability, and the latter provides an estimate of the feasibility of the plan.
We believe our method is broadly applicable in settings where expert demonstrations are available, flexibility to new situations is demanded, and safety is critical.
Figure 8 : Tolerating bad waypoints.
The planner prefers waypoints in the distribution of expert behavior: on the road at a reasonable distance.
Columns 1,2: Planning with 1 /2 decoy waypoints.
Columns 3,4: Planning with all waypoints on the wrong side of the road.

assistant: text: Hybrid Vision-Driven Imitation Learning and Model-Based Reinforcement Learning for Planning, Forecasting, and Control