# Robotics and execution traces

**Publish date:**Sep 17, 2022

The goal of this post is to describe the ideas behind Pylic, a work-in-progress Python library to describe and solve predicates over program execution traces.

*Pylic is a portmanteau of Python and concolic (as in concolic testing). The name is definitely temporary.*

## Motivation

Traditionally, control theory and Reinforcement Learning concern themselves with mathematical models of the environment (differential equations) or simulations (integrations of said differential equations). For instance, we may linearize dynamics and apply control algorithms like LQR or assume we have a step-wise reward function of a simulation and apply Reinforcement Learning. However, automatically dealing with structure –things like contacts, robot functionality difficult to capture in a mathematical model, or environment features like buttons, etc– is an unsolved problem.

I believe program analysis can allow the development of generic control algorithms that exploit structure that is otherwise invisible. For instance, program analysis can allow a generic control policy to:

Automatically discover things that a robot can do just from the simulator code.

Perform structured domain exploration, like pressing a button in a novel environment to see what it does without hard-coding this as a search objective.

Embrace discontinuities (e.g., contacts) in environment dynamics.

Transfer fine-tuned robot “brains” (i.e. parametric control policies) to new hardware platforms and automatically recognizing new functionality without losing previous fine-tuning.

Program analysis, I believe, can fulfill these goals because all these things –everything from their mere existence to the semantics of how things relate to each other– are represented in simulator code. Control algorithms need not reason only in terms of actuators and states but also in structured and generic representations like source-code.

While I do not yet have evidence that program analysis does lead to achieving these grand goals, in this document I hope to illustrate what a system that can exploit simulator source-code can look like.

## Pylic

Pylic is a solution to exposing simulator functionality and structure to control algorithms. It allows a control algorithm to say things like “find control parameters that lead to a simulation where property *P* is true”, and it does so in a language that allows *P* to refer to the simulator source-code.

The crucial difference between Pylic and traditional approaches like those based on temporal logics (and the way in which Pylic relates to the goals outlined in the motivation), is that **Pylic is designed to interface with the execution of the simulator itself**, not just the simulated system’s state. This difference is important, as the simulator naturally encodes semantics of the features in the robot and the environment.

Let us see how Pylic approaches this through an example of using its API.

### Bouncing marble

Assume we have a simulation of a thrusted bouncing marble in a 2D surface with obstacles. We wish to find a sequence of thrusts such that the marble reaches a target location while colliding with one of the obstacles (say obstacle 1).

“Colliding with obstacle 1” is a property of the execution of the simulation. To see this, note that in the simulation code there is a condition that checks whether the marble is colliding (see code below).

```
def simulation(
actions: torch.Tensor,
state: State,
) -> State:
"""Run the simulation with the given closed-loop control
signal and return the final state."""
for t in range(len(actions)):
# Compute next state with no collisions
action = actions[t]
next_state = normal_dynamics_force(
state,
action,
task.drag_constant,
task.dt
)
# Check for collisions
for i in range(len(state.obstacles)):
v = collision_soft_predicate(state, task.dt, i)
# This condition checks whether the marble
# is colliding:
if v <= 0:
# If collided, adjust next state
next_state = collision_dynamics(
state,
i,
task.dt,
task.coefficient_of_restitution
)
# Update state
state = next_state
return state
```

We see that “collide with obstacle 1” can be equivalently described as “make the collision if-statement true at least once”. This can further be formalized in propositional logic as `collision_1 OR collision_2 OR ... OR collision_T`

, where the index is the simulation time-step. Note that this is a predicate whose truth value is determined by our control parameters: some thrust sequences will steer the marble such that the predicate is true, and some others will make it false. Our goal is therefore to find some parameters that make the predicate true.

Pylic provides a language built on top of propositional logic to describe predicates over program traces. For instance, it’s API allows us to encode our desired behaviour as “collide with obstacle 1 within the first half of simulator AND reach the goal at the end”:

```
# This predicate encodes the goal of "colliding
# within the first half of the simulation, then
# reach the goal"
predicate = Conjunction([ # Should be true:
# (collide(t=0) OR collide(t=1) OR ...)
Disjunction([IfNodeFilter( # (1) at least one collision check where
id=None,
varspec=VarSpecConjunction([
VarEqualTo('i', 1), # obstacle index = 1
VarLessThan('t', timestep_n//2), # time is < first half of sim
]),
)]),
# (dist_to_goal[end_of_sim] < 0.1)
FilterComposition( # filter return state and
filter=NodeFilter(
type=NodeTypes.RETURN,
id=None,
varspec=VarSpecTop()
),
predicate=LessThan( # (2) make sure that
"dist_to_goal", # dist_to_goal < 0.01
torch.tensor(0.01),
),
)
])
```

We can then provide Pylic this predicate and instruct it to solve it in the simulation program. Pylic will then execute the simulation and search for parameters that make the predicate true.

```
# Solve the predicate with Pylic's gradient descent solver
starting_parameters = torch.zeros((task.max_timesteps, 2))
parameters, _ = solver(
predicate,
starting_parameters=starting_parameters,
f_args=initial_state,
f=simulation,
max_value=torch.tensor(10),
custom_interpreter=interpreter,
grad_mask=torch.ones_like(starting_parameters),
iter_n=2000,
learning_rate=0.01,
momentum_beta=0.1,
normalize_gradient=True,
verbose=True,
)
```

After a few minutes the solver returns some control parameters.

### What is under the hood?

Two core aspects of the functionality presented in this post are automatic program tracing and numerical optimization.

Tracing is done with Python’s meta-programming capabilities. Specifically, Pylic reads the source code of the simulation at run-time, parses it into an AST, and manipulates the AST to introduce tracing code, then the new code is lowered into a new function definition that is evaluated by the Python interpreter.

In the automatic tracing source-code transformation I took care to “soften” a subset of Python’s control-flow conditions. For instance, consider the problem of finding some floating point value `x`

that makes `if x < c`

true. If done naively (randomly sample `x`

values until `x < c`

is true) then this is an extremely challenging problem. However, we can instead transform the problem into maximizing `f(x) := c-x`

until we find a value for which `f(x) > 0`

, which can be done with numerical optimization algorithms. In Signal Temporal Logic and fuzzy logics this is called “quantitative semantics”, and the value of `f(x)`

is called the robustness value of the predicate.

Therefore, where possible, the trace then contains the robustness values of the control-flow conditions, instead of the boolean values that cannot be used. Then, we can use Pytorch (an operator-overloading automatic differentiation library) to compute the gradient with respect to the parameters and perform gradient-ascent and (hopefully) solve the predicate.

## Concluding remarks

Pylic is in very early stages of development. It aims to use source-code as the “common language” between control algorithms, domain experts and solvers. In this post I described predicates over execution traces as one mechanism to accomplish these goals.

As for the functionality presented in this post, there are a couple of very obvious limitations in the current implementation:

Gradient-based optimization is extremely scalable, but it is not effective in non-convex problems. This is unfortunate because it is very easy to wish for solutions of predicates whose robustness functions are non-convex.

Providing Pylic with gradient-free optimization algorithms (e.g., CMA-ES, CEM, GAs, etc) would allow the solver to find solutions to some of these predicates while maintaining scalability.

Another way to improve this is to use SMT-based solvers (e.g. Z3). However, it is not obvious how to encode generic Python programs with control-flow into z3. It is thus an open question whether SMT-based solvers integrate nicely with tracing-based approaches.

Currently only top-level functions are traced. There are multiple ways to solve this to different degrees (e.g. automatic function in-lining, manual decorator-based tracing, automatic transformation of called functions). PyTorch has some tracing functionality too, which could be integrated into Pylic.

I believe the functionality presented here is a step towards a platform that allows the design of control algorithms for autonomous systems that systematically explore their functionality and their environment.