File size: 2,211 Bytes
42d9709 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gymnasium as gym
import torch
from typing import Optional
from evaluators.evaluator_base import EvaluatorBase
from isaaclab.managers import TerminationTermCfg as DoneTerm
class Gr00tN1Evaluator(EvaluatorBase):
"""
The purpose of this class is to evaluate the performance of gr00t-N1 policy on the nut pouring
and pipe sorting tasks, by tracking the success rate of the policy over a series of demos.
Success is defined as termination term in the environment configuration script
"""
def __init__(self, checkpoint_name: str, eval_file_path: Optional[str] = None, seed: int = 10) -> None:
super().__init__(checkpoint_name, eval_file_path, seed)
self.num_success = 0
self.num_rollouts = 0
def evaluate_step(self, env: gym.Env, succeess_term: DoneTerm) -> None:
success_term_val = succeess_term.func(env, **succeess_term.params)
self.num_success += torch.sum(success_term_val).item()
self.num_rollouts += len(success_term_val)
def summarize_demos(self):
# printe in terminal with a table layout
print(f"\n{'='*50}")
print(f"\nSuccessful trials: {self.num_success}, out of {self.num_rollouts} trials")
print(f"Success rate: {self.num_success / self.num_rollouts}")
print(f"{'='*50}\n")
self.eval_dict["summary"] = {
"successful_trials": self.num_success,
"total_rollouts": self.num_rollouts,
"success_rate": self.num_success / self.num_rollouts,
}
|