Pros of Bullet3

• More mature and feature-rich physics engine with a wider range of simulation capabilities
• Extensive documentation and community support
• Better performance for large-scale simulations

Cons of Bullet3

• Larger codebase and more complex architecture
• Not specifically designed for differentiable simulations
• Steeper learning curve for new users

Code Comparison

Bullet3:

btDefaultCollisionConfiguration* collisionConfiguration = new btDefaultCollisionConfiguration();
btCollisionDispatcher* dispatcher = new btCollisionDispatcher(collisionConfiguration);
btBroadphaseInterface* overlappingPairCache = new btDbvtBroadphase();
btSequentialImpulseConstraintSolver* solver = new btSequentialImpulseConstraintSolver;
btDiscreteDynamicsWorld* dynamicsWorld = new btDiscreteDynamicsWorld(dispatcher, overlappingPairCache, solver, collisionConfiguration);

Tiny Differentiable Simulator:

TinyWorld world;
TinyVector3 gravity(0, -9.81, 0);
world.set_gravity(gravity);
TinyMultiBody mb;
world.add_multi_body(mb);

The Tiny Differentiable Simulator offers a more streamlined API focused on differentiable physics simulations, while Bullet3 provides a more comprehensive set of features for general-purpose physics simulations.

Pros of tiny-differentiable-simulator

• Lightweight and efficient implementation
• Supports differentiable physics simulations
• Easy to integrate with machine learning frameworks

Cons of tiny-differentiable-simulator

• Limited documentation and examples
• May lack some advanced features of larger physics engines
• Potentially less community support due to smaller user base

Code Comparison

tiny-differentiable-simulator:

TinyDifferentiableSimulator sim;
sim.add_body(mass, position, orientation);
sim.step(dt);
auto gradients = sim.compute_gradients();

tiny-differentiable-simulator:

TinyDifferentiableSimulator sim;
sim.add_body(mass, position, orientation);
sim.step(dt);
auto gradients = sim.compute_gradients();

As we can see, the code comparison shows identical snippets for both repositories. This is because the request asked to compare the same repository against itself. In a real-world scenario, we would expect to see differences in the code structure, function names, or implementation details between two distinct repositories.

Pros of MuJoCo

• More mature and widely adopted in the research community
• Extensive documentation and support
• Highly optimized for performance and stability

Cons of MuJoCo

• Larger codebase and more complex to modify
• Less focus on differentiability and automatic differentiation

Code Comparison

MuJoCo:

mjModel* m = mj_loadXML("model.xml", 0, error, 1000);
mjData* d = mj_makeData(m);
mj_step(m, d);

Tiny Differentiable Simulator:

TinyMultiBodyConstraintSolver solver;
TinyWorld world;
world.step(dt);

Key Differences

• MuJoCo uses a more traditional physics simulation approach, while Tiny Differentiable Simulator focuses on differentiability
• Tiny Differentiable Simulator is designed to be lightweight and easily integrated into machine learning frameworks
• MuJoCo offers a wider range of features and simulation capabilities

Use Cases

• MuJoCo: Ideal for complex robotics simulations and reinforcement learning research
• Tiny Differentiable Simulator: Better suited for projects requiring gradient-based optimization and differentiable physics

Community and Support

• MuJoCo: Large user base, active community, and extensive documentation
• Tiny Differentiable Simulator: Smaller community, but growing interest in differentiable physics simulations

Pros of FCL

• More comprehensive collision detection library with support for various geometric primitives and collision algorithms
• Well-established and widely used in robotics and simulation applications
• Provides both continuous and discrete collision detection capabilities

Cons of FCL

• Larger and more complex codebase, potentially harder to integrate or modify
• May have higher computational overhead for simpler collision scenarios
• Not specifically designed for differentiable simulations

Code Comparison

FCL:

#include <fcl/fcl.h>

fcl::CollisionObjectd* obj1 = new fcl::CollisionObjectd(std::make_shared<fcl::Boxd>(1, 1, 1));
fcl::CollisionObjectd* obj2 = new fcl::CollisionObjectd(std::make_shared<fcl::Sphered>(0.5));
fcl::CollisionRequestd request;
fcl::CollisionResultd result;
fcl::collide(obj1, obj2, request, result);

Tiny Differentiable Simulator:

#include "tiny_differentiable_simulator.h"

TinyVector3f pos1(0, 0, 0), pos2(1, 1, 1);
TinyVector3f half_extents(0.5, 0.5, 0.5);
TinyCollisionObject obj1(TINY_BOX_TYPE, pos1, half_extents);
TinyCollisionObject obj2(TINY_SPHERE_TYPE, pos2, 0.5);
bool collision = tds::collide(obj1, obj2);